Codebook Offline Data

knitr::opts_chunk$set(
  warning = TRUE, # show warnings during codebook generation
  message = TRUE, # show messages during codebook generation
  error = TRUE, # do not interrupt codebook generation in case of errors,
                # usually better for debugging
  echo = TRUE  # show R code
)
ggplot2::theme_set(ggplot2::theme_bw())
pander::panderOptions("table.split.table", Inf)

library(codebook)
library(labelled)
#codebook_data <- codebook::bfi
# to import an SPSS file from the same folder uncomment and edit the line below
# codebook_data <- rio::import("mydata.sav")
# for Stata
# codebook_data <- rio::import("mydata.dta")
# for CSV
codebook_data <- rio::import("offline_data.csv")

# omit the following lines, if your missing values are already properly labelled
codebook_data <- detect_missing(codebook_data,
    only_labelled = TRUE, # only labelled values are autodetected as
                                   # missing
    negative_values_are_missing = FALSE, # negative values are missing values
    ninety_nine_problems = TRUE   # 99/999 are missing values, if they
                                   # are more than 5 MAD from the median
    )

# If you are not using formr, the codebook package needs to guess which items
# form a scale. The following line finds item aggregates with names like this:
# scale = scale_1 + scale_2R + scale_3R
# identifying these aggregates allows the codebook function to
# automatically compute reliabilities.
# However, it will not reverse items automatically.
#codebook_data <- detect_scales(codebook_data)
# label variables correctly
#  

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

####   removing doubled column baby anchoring and scale mean scores, as the latter are created anyways  ####
drop <- c("anc_baby", "E", "A", "C", "N", "O", "soc_d_mean")
codebook_data = codebook_data[ , !(names(codebook_data) %in% drop)]
var_label(codebook_data) <- list(
        gender = "Reported gender information",
        acad_status = "Academic status", 
        age = "Age Group", 
        refused = "Refused participation after consent",
        reason = "Reason for refusal", 
        consent = "Data sharing policy in consent", 
        bf_1 = "Big 5 Extraversion item 1", 
        bf_2 = "Big 5 Agreeableness item 2", 
        bf_3 = "Big 5 Conscientiousness item 3", 
        bf_4 = "Big 5 Neuroticism item 4", 
        bf_5 = "Big 5 Openness to experience item 5", 
        bf_6 = "Big 5 Extraversion item 6", 
        bf_7 = "Big 5 Agreeableness item 7", 
        bf_8 = "Big 5 Conscientiousness item 8", 
        bf_9 = "Big 5 Neuroticism item 9", 
        bf_10 = "Big 5 Openness to experience item 10", 
        bf_11 = "Big 5 Extraversion item 11", 
        bf_12 = "Big 5 Agreeableness item 12", 
        bf_13 = "Big 5 Conscientiousness item 13", 
        bf_14 = "Big 5 Neuroticism item 14", 
        bf_15 = "Big 5 Openness to experience item 15", 
        bf_16 = "Big 5 Extraversion item 16", 
        bf_17 = "Big 5 Agreeableness item 17", 
        bf_18 = "Big 5 Conscientiousness item 18", 
        bf_19 = "Big 5 Neuroticism item 19", 
        bf_20 = "Big 5 Openness to experience item 20", 
        bf_21 = "Big 5 Extraversion item 21", 
        bf_22 = "Big 5 Agreeableness item 22", 
        bf_23 = "Big 5 Conscientiousness item 23", 
        bf_24 = "Big 5 Neuroticism item 24", 
        bf_25 = "Big 5 Openness to experience item 25", 
        bf_26 = "Big 5 Extraversion item 26", 
        bf_27 = "Big 5 Agreeableness item 27", 
        bf_28 = "Big 5 Conscientiousness item 28", 
        bf_29 = "Big 5 Neuroticism item 29", 
        bf_30 = "Big 5 Openness to experience item 30", 
        bf_31 = "Big 5 Extraversion item 31", 
        bf_32 = "Big 5 Agreeableness item 32", 
        bf_33 = "Big 5 Conscientiousness item 33", 
        bf_34 = "Big 5 Neuroticism item 34", 
        bf_35 = "Big 5 Openness to experience item 35", 
        bf_36 = "Big 5 Extraversion item 36", 
        bf_37 = "Big 5 Agreeableness item 37", 
        bf_38 = "Big 5 Conscientiousness item 38", 
        bf_39 = "Big 5 Neuroticism item 39", 
        bf_40 = "Big 5 Openness to experience item 40", 
        bf_41 = "Big 5 Openness to experience item 41", 
        bf_42 = "Big 5 Agreeableness item 42", 
        bf_43 = "Big 5 Conscientiousness item 43", 
        bf_44 = "Big 5 Openness to experience item 44", 
        bf_45 = "Big 5 Agreeableness item 45", 
        cr_1 = "Careless response item 1", 
        cr_2 = "Careless response item 2", 
        cr_3 = "Careless response item 3", 
        cr_4 = "Careless response item 4", 
        cr_5 = "Careless response item 5", 
        cr_6 = "Careless response item 6", 
        cr_7 = "Careless response item 7", 
        soc_d_1 = "Social desirability item 1", 
        soc_d_2 = "Social desirability item 2", 
        soc_d_3 = "Social desirability item 3", 
        soc_d_4 = "Social desirability item 4", 
        soc_d_5 = "Social desirability item 5", 
        soc_d_6 = "Social desirability item 6", 
        soc_d_7 = "Social desirability item 7", 
        soc_d_8 = "Social desirability item 8", 
        soc_d_9 = "Social desirability item 9", 
        soc_d_10 = "Social desirability item 10", 
        soc_d_11 = "Social desirability item 11", 
        soc_d_12 = "Social desirability item 12", 
        soc_d_13 = "Social desirability item 13", 
        soc_d_14 = "Social desirability item 14", 
        soc_d_15 = "Social desirability item 15", 
        soc_d_16 = "Social desirability item 16", 
        soc_d_17 = "Social desirability item 17", 
        soc_d_18 = "Social desirability item 18", 
        cond_anc = "Anchoring condition", 
        anc_everest = "Anchoring: How high is Mount Everest", 
        anc_chicago = "Anchoring: Population of Chicagor", 
        anc_bebe = "Anchoring: Babies born in US", 
         mc_1 = "Manipulation check, question 1: 'Do you remember the consent you signed in the beginning?'", 
        mc_2 = "Manipulation check, question 2: 'Do you remember if the consent contained the topic of sharing anonymous data with others?' ", 
        mc_3 = "Manipulation check, question: 'Will your anonymous data be shared with others?'", 
        # mc_1", 
        # mc_2", 
        # mc_3", 
        remarks = "Observer remarks"#, 
        # anc_baby", 
        # E = "Mean score on Extraversion subscale", 
        # A = "Mean score on Agreeableness subscale", 
        # C = "Mean score on Conscientiousness subscale", 
        # N = "Mean score on Neuroticism subscale", 
        # O = "Mean score on Openness to experience subscale", 
        # soc_d_mean = "Mean score on Social desirability scale"
)

val_labels(codebook_data$gender) <- c("Female" = 1, "Male" = 2)
val_labels(codebook_data$acad_status) <- c("other" = 0, "bachelor" = 1, "master" = 2, "PhD" = 3, "other" = 4)
val_labels(codebook_data$age) <- c("18-29 yo" = 1, "21-25 yo" = 2, "26-30 yo" = 3, "31-35 yo" = 4, "36-40 yo" = 5, "41-50 yo" = 6, "51-60 yo" = 7, "61-70 yo" = 8, "71-80 yo" = 9, "81-110 yo" = 10)
val_labels(codebook_data$refused) <- c("No" = 0, "Yes" = 1)
val_labels(codebook_data$consent) <- c("Data will be shared" = "A", "Data will not be shared" = "B")
val_labels(codebook_data$cond_anc) <- c("Low anchoring value" = 0, "High anchoring value" = 1)
val_labels(codebook_data$reason) <- c("Not refused" = 0, "Data sharing" = 1, "Not enough time" = 2, "Other" = 3)
val_labels(codebook_data$mc_1) <- c("Yes" = 1, "No" = 0)
val_labels(codebook_data$mc_2) <- c("Yes" = 1, "No" = 0)
val_labels(codebook_data$mc_3) <- c("Yes" = 1, "No" = 0)

add_likert_labels <- function(x) {
  val_labels(x) <- c("Disapprove strongly" = 1, 
                  "Disapprove slightly" = 2, 
                  "Neither approve nore disapprove" = 3,
                  "Approve slightly" = 4,
                  "Approve strongly" = 5)
  x
}
likert_items <- names(codebook_data[, c(10:79) ])
codebook_data <- codebook_data %>% mutate_at(likert_items, add_likert_labels)

####     Extraversion     ####
codebook_data$Extraversion <- codebook_data %>% select("bf_1", "bf_6", "bf_11", "bf_16", "bf_21", "bf_26", 
           "bf_31", "bf_36") %>% aggregate_and_document_scale()

reversed_items <- c("bf_6", "bf_21", "bf_31")

codebook_data <- codebook_data %>% 
  rename_at(reversed_items,  add_R)

codebook_data <- codebook_data %>% 
     mutate_at(vars(matches("\\dR$")), reverse_labelled_values)
 
codebook_data$Extraversion <- codebook_data %>% select("bf_1", "bf_6R", "bf_11", "bf_16", "bf_21R", "bf_26", 
           "bf_31R", "bf_36") %>% aggregate_and_document_scale()

####    Agreeableness    ####
codebook_data$Agreeableness <- codebook_data %>% select("bf_2", "bf_7", "bf_12", "bf_17", "bf_22", 
           "bf_27", "bf_32", "bf_37", "bf_42", "bf_45") %>% aggregate_and_document_scale()

reversed_items <- c("bf_2", "bf_12", "bf_27", "bf_37", "bf_45")

codebook_data <- codebook_data %>% 
  rename_at(reversed_items,  add_R)

codebook_data <- codebook_data %>% 
     mutate_at(vars(matches("\\dR$")), reverse_labelled_values)
 
codebook_data$Agreeableness <- codebook_data %>% select("bf_2R", "bf_7", "bf_12R", "bf_17", "bf_22", 
           "bf_27R", "bf_32", "bf_37R", "bf_42", "bf_45R") %>% aggregate_and_document_scale()

####    Conscientiousness    ####
codebook_data$Conscientiousness <- codebook_data %>% select("bf_3", "bf_8","bf_13", "bf_18", "bf_23", "bf_28", 
           "bf_33", "bf_38", "bf_43") %>% aggregate_and_document_scale()

reversed_items <- c("bf_8", "bf_18", "bf_23", "bf_38", "bf_43")

codebook_data <- codebook_data %>% 
  rename_at(reversed_items,  add_R)

codebook_data <- codebook_data %>% 
     mutate_at(vars(matches("\\dR$")), reverse_labelled_values)
 
codebook_data$Conscientiousness <- codebook_data %>% select("bf_3", "bf_8R","bf_13", "bf_18R", "bf_23R", "bf_28", 
           "bf_33", "bf_38R", "bf_43R") %>% aggregate_and_document_scale()

####    Neuroticism    ####
codebook_data$Neuroticism <- codebook_data %>% select("bf_4", "bf_9", "bf_14", "bf_19", "bf_24", "bf_29", 
           "bf_34", "bf_39") %>% aggregate_and_document_scale()

reversed_items <- c("bf_9", "bf_24", "bf_34")

codebook_data <- codebook_data %>% 
  rename_at(reversed_items,  add_R)

codebook_data <- codebook_data %>% 
     mutate_at(vars(matches("\\dR$")), reverse_labelled_values)
 
codebook_data$Neuroticism <- codebook_data %>% select("bf_4", "bf_9R", "bf_14", "bf_19", "bf_24R", "bf_29", 
           "bf_34R", "bf_39") %>% aggregate_and_document_scale()

####    Openness to experience    ####
codebook_data$'Openness to experience' <- codebook_data %>% select("bf_5", "bf_10", "bf_15", "bf_20", "bf_25", "bf_30", 
           "bf_35", "bf_40", "bf_41", "bf_44") %>% aggregate_and_document_scale()

reversed_items <- c("bf_35", "bf_41")

codebook_data <- codebook_data %>% 
  rename_at(reversed_items,  add_R)

codebook_data <- codebook_data %>% 
     mutate_at(vars(matches("\\dR$")), reverse_labelled_values)
 
codebook_data$'Openness to experience' <- codebook_data %>% select("bf_5", "bf_10", "bf_15", "bf_20", "bf_25", "bf_30", 
           "bf_35R", "bf_40", "bf_41R", "bf_44") %>% aggregate_and_document_scale()

####    Hétéroduperie/Social Desirability    ####
codebook_data$'Heteroduperie - Social Desirability' <- codebook_data %>% select("soc_d_1",
         "soc_d_2", "soc_d_3", "soc_d_4","soc_d_5", "soc_d_6",
         "soc_d_7", "soc_d_8", "soc_d_9", "soc_d_10",
         "soc_d_11", "soc_d_12", "soc_d_13", "soc_d_14", 
         "soc_d_15", "soc_d_16", "soc_d_17", "soc_d_18") %>% aggregate_and_document_scale()

reversed_items <- c("soc_d_2", "soc_d_4", "soc_d_5", "soc_d_7",
                    "soc_d_10", "soc_d_11", "soc_d_13", "soc_d_14",
                    "soc_d_17")

codebook_data <- codebook_data %>% 
  rename_at(reversed_items,  add_R)

codebook_data <- codebook_data %>% 
     mutate_at(vars(matches("\\dR$")), reverse_labelled_values)
 
codebook_data$'Heteroduperie - Social Desirability' <- codebook_data %>% select("soc_d_1",
         "soc_d_2R", "soc_d_3", "soc_d_4R", "soc_d_5R", 
         "soc_d_6", "soc_d_7R", "soc_d_8", "soc_d_9", 
         "soc_d_10R", "soc_d_11R", "soc_d_12", "soc_d_13R",
         "soc_d_14R", "soc_d_15", "soc_d_16", "soc_d_17R",
         "soc_d_18") %>% aggregate_and_document_scale()

metadata(codebook_data)$name <- "French-Belgian Student data on Big 5, Social Desirability as measured by Heteroduperie and Anchoring Paradigm, entire data set"
metadata(codebook_data)$description <- "45 items taking from the french translation of the Big 5 Personality questionnaire (Plaisant et al. 2010), 18 items from the subscale 'Hétéroduperie' of the french social desirability scale (Tournois et al., 2010) and 3 Anchoring paradigm items as used in the ManyLabs replication project (Klein et al., 2014). Also includes 7 careless response items based on Meade and Craig (2012)"
metadata(codebook_data)$identifier <- "https://dx.doi.org/10.17605/OSF.IO/X25D3"
metadata(codebook_data)$creator <- "Julia C. Eberlen, Emmanuel Nicaise, Sarah Leveaux, Youri L. Mora, Olivier Klein"
metadata(codebook_data)$citation <- "Eberlen, J. C., Nicaise, E., Leveaux, S., Mora, Y., & Klein, O. (2019, August 5). Impact of Data sharing: Data collected offline. https://doi.org/10.17605/OSF.IO/X25D3"
metadata(codebook_data)$datePublished <- "2019-08-05"
metadata(codebook_data)$temporalCoverage <- "2018-12-03 to 2018-12-17" 
metadata(codebook_data)$spatialCoverage <- "Campus Solbosch, Universite libre de Bruxelles, Brussels, Belgium" 


codebook(codebook_data)

knitr::asis_output(data_info)

Metadata

Description

if (exists("name", meta)) {
  glue::glue(
    "__Dataset name__: {name}",
    .envir = meta)
}

Dataset name: French-Belgian Student data on Big 5, Social Desirability as measured by Heteroduperie and Anchoring Paradigm, entire data set

cat(description)

45 items taking from the french translation of the Big 5 Personality questionnaire (Plaisant et al. 2010), 18 items from the subscale ‘Hétéroduperie’ of the french social desirability scale (Tournois et al., 2010) and 3 Anchoring paradigm items as used in the ManyLabs replication project (Klein et al., 2014). Also includes 7 careless response items based on Meade and Craig (2012)

Metadata for search engines

• Temporal Coverage: 2018-12-03 to 2018-12-17
• Spatial Coverage: Campus Solbosch, Universite libre de Bruxelles, Brussels, Belgium

• Citation: Eberlen, J. C., Nicaise, E., Leveaux, S., Mora, Y., & Klein, O. (2019, August 5). Impact of Data sharing: Data collected offline. https://doi.org/10.17605/OSF.IO/X25D3

• Identifier: https://dx.doi.org/10.17605/OSF.IO/X25D3

• Date published: 2019-08-05

• Creator:Julia C. Eberlen, Emmanuel Nicaise, Sarah Leveaux, Youri L. Mora, Olivier Klein

meta <- meta[setdiff(names(meta),
                     c("creator", "datePublished", "identifier",
                       "url", "citation", "spatialCoverage", 
                       "temporalCoverage", "description", "name"))]
pander::pander(meta)

• keywords: V1, id, participant, gender, acad_status, age, refused, reason, consent, bf_1, bf_2R, bf_3, bf_4, bf_5, bf_6R, bf_7, bf_8R, bf_9R, bf_10, bf_11, bf_12R, bf_13, bf_14, bf_15, bf_16, bf_17, bf_18R, bf_19, bf_20, bf_21R, bf_22, bf_23R, bf_24R, bf_25, bf_26, bf_27R, bf_28, bf_29, bf_30, bf_31R, bf_32, bf_33, bf_34R, bf_35R, bf_36, bf_37R, bf_38R, bf_39, bf_40, bf_41R, bf_42, bf_43R, bf_44, bf_45R, cr_1, cr_2, cr_3, cr_4, cr_5, cr_6, cr_7, soc_d_1, soc_d_2R, soc_d_3, soc_d_4R, soc_d_5R, soc_d_6, soc_d_7R, soc_d_8, soc_d_9, soc_d_10R, soc_d_11R, soc_d_12, soc_d_13R, soc_d_14R, soc_d_15, soc_d_16, soc_d_17R, soc_d_18, cond_anc, anc_everest, anc_chicago, anc_bebe, mc_1, mc_2, mc_3, remarks, Extraversion, Agreeableness, Conscientiousness, Neuroticism, Openness to experience and Heteroduperie - Social Desirability

knitr::asis_output(survey_overview)

Variables

if (detailed_variables || detailed_scales) {
  knitr::asis_output(paste0(scales_items, sep = "\n\n\n", collapse = "\n\n\n"))
}

V1

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

0 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	data_type	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
V1	integer	0	193	193	97	55.86	1	49	97	145	193	▇▇▇▇▇▇▇▇

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

id

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

0 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	data_type	missing	complete	n	empty	n_unique	min	max
id	character	0	193	193	0	193	4	5

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

participant

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

0 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	data_type	missing	complete	n	empty	n_unique	min	max
participant	character	0	193	193	0	193	4	5

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

gender

Reported gender information

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

1 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
gender	Reported gender information	integer	1. Female, 2. Male	1	192	193	1.35	0.48	1	1	1	2	2	▇▁▁▁▁▁▁▅

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

Value labels

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

• Female: 1
• Male: 2

acad_status

Academic status

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

1 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
acad_status	Academic status	integer	0. other, 1. bachelor, 2. master, 3. PhD, 4. other	1	192	193	1.22	0.52	0	1	1	2	3	▁▁▇▁▁▃▁▁

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

Value labels

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

• other: 0
• bachelor: 1
• master: 2
• PhD: 3
• other: 4

age

Age Group

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

1 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
age	Age Group	integer	1. 18-29 yo, 2. 21-25 yo, 3. 26-30 yo, 4. 31-35 yo, 5. 36-40 yo, 6. 41-50 yo, 7. 51-60 yo, 8. 61-70 yo, 9. 71-80 yo, 10. 81-110 yo	1	192	193	1.66	0.8	1	1	2	2	7	▇▆▂▁▁▁▁▁

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

Value labels

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

• 18-29 yo: 1
• 21-25 yo: 2
• 26-30 yo: 3
• 31-35 yo: 4
• 36-40 yo: 5
• 41-50 yo: 6
• 51-60 yo: 7
• 61-70 yo: 8
• 71-80 yo: 9
• 81-110 yo: 10

refused

Refused participation after consent

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

0 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
refused	Refused participation after consent	integer	0. No, 1. Yes	0	193	193	0.0052	0.072	0	0	0	0	1	▇▁▁▁▁▁▁▁

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

Value labels

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

• No: 0
• Yes: 1

reason

Reason for refusal

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

0 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
reason	Reason for refusal	integer	0. Not refused, 1. Data sharing, 2. Not enough time, 3. Other	0	193	193	0.0052	0.072	0	0	0	0	1	▇▁▁▁▁▁▁▁

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

Value labels

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

• Not refused: 0
• Data sharing: 1
• Not enough time: 2
• Other: 3

consent

Data sharing policy in consent

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

0 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	value_labels	missing	complete	n	empty	n_unique	min	max
consent	Data sharing policy in consent	character	A. Data will be shared, B. Data will not be shared	0	193	193	0	2	1	1

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

Value labels

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

• Data will be shared: A
• Data will not be shared: B

cr_1

Careless response item 1

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

2 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
cr_1	Careless response item 1	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	2	191	193	1.23	0.64	1	1	1	1	5	▇▁▁▁▁▁▁▁

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

Value labels

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

• Disapprove strongly: 1
• Disapprove slightly: 2
• Neither approve nore disapprove: 3
• Approve slightly: 4
• Approve strongly: 5

cr_2

Careless response item 2

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

4 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
cr_2	Careless response item 2	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	4	189	193	1.69	1.15	1	1	1	2	5	▇▁▁▂▁▁▁▁

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

Value labels

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

• Disapprove strongly: 1
• Disapprove slightly: 2
• Neither approve nore disapprove: 3
• Approve slightly: 4
• Approve strongly: 5

cr_3

Careless response item 3

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

3 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
cr_3	Careless response item 3	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	3	190	193	1.69	1.14	1	1	1	2	5	▇▁▁▂▁▁▁▁

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

Value labels

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

• Disapprove strongly: 1
• Disapprove slightly: 2
• Neither approve nore disapprove: 3
• Approve slightly: 4
• Approve strongly: 5

cr_4

Careless response item 4

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

1 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
cr_4	Careless response item 4	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	1	192	193	1.2	0.62	1	1	1	1	5	▇▁▁▁▁▁▁▁

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

Value labels

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

• Disapprove strongly: 1
• Disapprove slightly: 2
• Neither approve nore disapprove: 3
• Approve slightly: 4
• Approve strongly: 5

cr_5

Careless response item 5

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

1 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
cr_5	Careless response item 5	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	1	192	193	1.15	0.58	1	1	1	1	5	▇▁▁▁▁▁▁▁

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

Value labels

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

• Disapprove strongly: 1
• Disapprove slightly: 2
• Neither approve nore disapprove: 3
• Approve slightly: 4
• Approve strongly: 5

cr_6

Careless response item 6

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

4 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
cr_6	Careless response item 6	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	4	189	193	1.57	1.28	1	1	1	1	5	▇▁▁▁▁▁▁▁

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

Value labels

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

• Disapprove strongly: 1
• Disapprove slightly: 2
• Neither approve nore disapprove: 3
• Approve slightly: 4
• Approve strongly: 5

cr_7

Careless response item 7

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

4 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
cr_7	Careless response item 7	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	4	189	193	1.48	1.17	1	1	1	1	5	▇▁▁▁▁▁▁▁

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

Value labels

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

• Disapprove strongly: 1
• Disapprove slightly: 2
• Neither approve nore disapprove: 3
• Approve slightly: 4
• Approve strongly: 5

cond_anc

Anchoring condition

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

1 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
cond_anc	Anchoring condition	integer	0. Low anchoring value, 1. High anchoring value	1	192	193	0.52	0.5	0	0	1	1	1	▇▁▁▁▁▁▁▇

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

Value labels

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

• Low anchoring value: 0
• High anchoring value: 1

anc_everest

Anchoring: How high is Mount Everest

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

12 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
anc_everest	Anchoring: How high is Mount Everest	integer	12	181	193	28134.86	259715.18	600	3200	8000	11000	3500000	▇▁▁▁▁▁▁▁

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

anc_chicago

Anchoring: Population of Chicagor

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

12 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
anc_chicago	Anchoring: Population of Chicagor	numeric	12	181	193	19.92	185.67	0.004	1.5	3.5	7	2500	▇▁▁▁▁▁▁▁

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

anc_bebe

Anchoring: Babies born in US

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

13 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
anc_bebe	Anchoring: Babies born in US	integer	13	180	193	68125.47	163124.78	40	1000	20000	50000	1e+06	▇▁▁▁▁▁▁▁

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

mc_1

Manipulation check, question 1: ‘Do you remember the consent you signed in the beginning?’

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

4 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
mc_1	Manipulation check, question 1: ‘Do you remember the consent you signed in the beginning?’	integer	1. Yes, 0. No	4	189	193	0.98	0.14	0	1	1	1	1	▁▁▁▁▁▁▁▇

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

Value labels

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

• Yes: 1
• No: 0

mc_2

Manipulation check, question 2: ‘Do you remember if the consent contained the topic of sharing anonymous data with others?’

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

3 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
mc_2	Manipulation check, question 2: ‘Do you remember if the consent contained the topic of sharing anonymous data with others?’	integer	1. Yes, 0. No	3	190	193	0.74	0.44	0	0	1	1	1	▃▁▁▁▁▁▁▇

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

Value labels

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

• Yes: 1
• No: 0

mc_3

Manipulation check, question: ‘Will your anonymous data be shared with others?’

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

5 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
mc_3	Manipulation check, question: ‘Will your anonymous data be shared with others?’	integer	1. Yes, 0. No	5	188	193	0.56	0.5	0	0	1	1	1	▆▁▁▁▁▁▁▇

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

Value labels

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

• Yes: 1
• No: 0

remarks

Observer remarks

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

0 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	missing	complete	n	empty	n_unique	min	max
remarks	Observer remarks	character	0	193	193	168	26	0	95

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

Scale: Extraversion

Overview

Reliability: ω_ordinal [95% CI] = 0.3 [0.19;0.4].

Missing: 5.

old_height <- knitr::opts_chunk$get("fig.height")
new_height <- length(scale_info$scale_item_names)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
new_height <- ifelse(is.na(new_height) | is.nan(new_height), 
                     old_height, new_height)
knitr::opts_chunk$set(fig.height = new_height)

if (dplyr::n_distinct(na.omit(unlist(items))) < 12) {
  likert_plot <- likert_from_items(items)
  if (!is.null(likert_plot)) {
    graphics::plot(likert_plot)
  }
}

knitr::opts_chunk$set(fig.height = old_height)

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

dist_plot <- plot_labelled(scale, scale_name, wrap_at)

choices <- attributes(items[[1]])$item$choices
breaks <- as.numeric(names(choices))
if (length(breaks)) {
  suppressMessages( # ignore message about overwriting x axis
  dist_plot <- dist_plot +
        ggplot2::scale_x_continuous("values", 
                                breaks = breaks, 
                                labels = stringr::str_wrap(unlist(choices), ceiling(wrap_at * 0.21))) +
      ggplot2::expand_limits(x = range(breaks)))
  
}

dist_plot

Reliability details

for (i in seq_along(reliabilities)) {
  rel <- reliabilities[[i]]
  cat(knitr::knit_print(rel, indent = paste0(indent, "####")))
}

Reliability Indices

coefs <- x$scaleReliability$output$dat %>% 
  tidyr::gather(index, estimate) %>% 
  dplyr::filter(index != "n.items", index != "n.observations") %>% 
  dplyr::mutate(index = stringr::str_to_title(
    stringr::str_replace_all(index,
      stringr::fixed("."), " ")))

cis <- coefs %>% 
  dplyr::filter(stringr::str_detect(index, " Ci ")) %>% 
  tidyr::separate(index, c("index", "hilo"), sep = " Ci ") %>% 
  tidyr::spread(hilo, estimate)
if (nrow(cis)) {
  cis <- cis %>% dplyr::rename(
    `Lower 95% CI` = .data$Lo, `Upper 95% CI` = .data$Hi
  )
}

coefs_with_cis <- coefs %>% 
  dplyr::filter(!stringr::str_detect(index, " Ci ")) %>% 
    dplyr::left_join(cis, by = "index") %>% 
    dplyr::mutate(index = dplyr::if_else(index == "Glb", "Greatest Lower Bound", .data$index)) %>% 
    dplyr::arrange(!stringr::str_detect(index, "Omega")) %>% 
    dplyr::select(Index = .data$index, Estimate = .data$estimate)


pander::pander(coefs_with_cis)

Index	Estimate
Omega	0.1528
Omega Psych Tot	0.9193
Omega Psych H	0.783
Omega Ordinal	0.296
Cronbach Alpha	-0.5445
Greatest Lower Bound	0.1591
Alpha Ordinal	-0.3017

Positive correlations: 13 out of 28 (46%)

Scatter matrix

print(x$scatterMatrix$output$scatterMatrix)

x$scatterMatrix$output$scatterMatrix <- no_md()

Detailed output

print(x)

## 
## Information about this analysis:
## 
##                  Dataframe: res$dat
##                      Items: bf_1, bf_6R, bf_11, bf_16, bf_21R, bf_26, bf_31R, bf_36
##               Observations: 188
##      Positive correlations: 13 out of 28 (46%)
## 
## Estimates assuming interval level:
## 
##              Omega (total): 0.15
##       Omega (hierarchical): 0.78
##    Revelle's omega (total): 0.92
## Greatest Lower Bound (GLB): 0.16
##              Coefficient H: 0.92
##           Cronbach's alpha: -0.54
## Confidence intervals:
##              Omega (total): [0.05, 0.25]
##           Cronbach's alpha: [0, -0.26]
## 
## Estimates assuming ordinal level:
## 
##      Ordinal Omega (total): 0.3
##  Ordinal Omega (hierarch.): 0.24
##   Ordinal Cronbach's alpha: -0.3
## Confidence intervals:
##      Ordinal Omega (total): [0.19, 0.4]
##   Ordinal Cronbach's alpha: [0, -0.02]
## 
## Note: the normal point estimate and confidence interval for omega are based on the procedure suggested by Dunn, Baguley & Brunsden (2013) using the MBESS function ci.reliability, whereas the psych package point estimate was suggested in Revelle & Zinbarg (2008). See the help ('?scaleStructure') for more information.
## 
## Eigen values: 4.578, 0.785, 0.664, 0.572, 0.52, 0.379, 0.277, 0.225
## Loadings:
##        PC1   
## bf_1    0.729
## bf_6R  -0.765
## bf_11   0.652
## bf_16   0.663
## bf_21R -0.851
## bf_26   0.731
## bf_31R -0.737
## bf_36   0.891
## 
##                  PC1
## SS loadings    4.578
## Proportion Var 0.572
## 
##        vars   n mean   sd median trimmed  mad min max range  skew kurtosis
## bf_1      1 188 3.54 1.18      4    3.62 1.48   1   5     4 -0.44    -0.72
## bf_6R     2 188 3.07 1.34      3    3.09 1.48   1   5     4 -0.17    -1.19
## bf_11     3 188 3.60 1.06      4    3.65 1.48   1   5     4 -0.41    -0.60
## bf_16     4 188 3.65 1.11      4    3.74 1.48   1   5     4 -0.58    -0.52
## bf_21R    5 188 2.71 1.33      3    2.66 1.48   1   5     4  0.05    -1.29
## bf_26     6 188 3.40 1.14      4    3.46 1.48   1   5     4 -0.40    -0.65
## bf_31R    7 188 2.98 1.33      3    2.98 1.48   1   5     4 -0.15    -1.22
## bf_36     8 188 3.26 1.26      3    3.32 1.48   1   5     4 -0.17    -1.00
##          se
## bf_1   0.09
## bf_6R  0.10
## bf_11  0.08
## bf_16  0.08
## bf_21R 0.10
## bf_26  0.08
## bf_31R 0.10
## bf_36  0.09

Summary statistics

for (i in seq_along(names(items))) {
  attributes(items[[i]]) = recursive_escape(attributes(items[[i]]))
}
escaped_table(codebook_table(items))

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
bf_1	Big 5 Extraversion item 1	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	2	191	193	3.57	1.19	1	3	4	5	5	▂▃▁▆▁▇▁▇
bf_6R	Big 5 Extraversion item 6	numeric	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	1	192	193	3.06	1.34	1	2	3	4	5	▅▅▁▆▁▇▁▅
bf_11	Big 5 Extraversion item 11	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	1	192	193	3.62	1.06	1	3	4	4	5	▁▃▁▆▁▇▁▅
bf_16	Big 5 Extraversion item 16	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	2	191	193	3.68	1.11	1	3	4	5	5	▁▃▁▃▁▇▁▆
bf_21R	Big 5 Extraversion item 21	numeric	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	3	190	193	2.72	1.32	1	1	3	4	5	▇▅▁▆▁▇▁▂
bf_26	Big 5 Extraversion item 26	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	1	192	193	3.41	1.13	1	3	4	4	5	▂▃▁▆▁▇▁▃
bf_31R	Big 5 Extraversion item 31	numeric	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	1	192	193	2.99	1.33	1	2	3	4	5	▅▅▁▆▁▇▁▃
bf_36	Big 5 Extraversion item 36	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	2	191	193	3.27	1.26	1	2	3	4	5	▃▅▁▇▁▆▁▆

Scale: Agreeableness

Overview

Reliability: ω_ordinal [95% CI] = 0.8 [0.76;0.84].

Missing: 4.

old_height <- knitr::opts_chunk$get("fig.height")
new_height <- length(scale_info$scale_item_names)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
new_height <- ifelse(is.na(new_height) | is.nan(new_height), 
                     old_height, new_height)
knitr::opts_chunk$set(fig.height = new_height)

if (dplyr::n_distinct(na.omit(unlist(items))) < 12) {
  likert_plot <- likert_from_items(items)
  if (!is.null(likert_plot)) {
    graphics::plot(likert_plot)
  }
}

knitr::opts_chunk$set(fig.height = old_height)

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

dist_plot <- plot_labelled(scale, scale_name, wrap_at)

choices <- attributes(items[[1]])$item$choices
breaks <- as.numeric(names(choices))
if (length(breaks)) {
  suppressMessages( # ignore message about overwriting x axis
  dist_plot <- dist_plot +
        ggplot2::scale_x_continuous("values", 
                                breaks = breaks, 
                                labels = stringr::str_wrap(unlist(choices), ceiling(wrap_at * 0.21))) +
      ggplot2::expand_limits(x = range(breaks)))
  
}

dist_plot

Reliability details

for (i in seq_along(reliabilities)) {
  rel <- reliabilities[[i]]
  cat(knitr::knit_print(rel, indent = paste0(indent, "####")))
}

Reliability Indices

coefs <- x$scaleReliability$output$dat %>% 
  tidyr::gather(index, estimate) %>% 
  dplyr::filter(index != "n.items", index != "n.observations") %>% 
  dplyr::mutate(index = stringr::str_to_title(
    stringr::str_replace_all(index,
      stringr::fixed("."), " ")))

cis <- coefs %>% 
  dplyr::filter(stringr::str_detect(index, " Ci ")) %>% 
  tidyr::separate(index, c("index", "hilo"), sep = " Ci ") %>% 
  tidyr::spread(hilo, estimate)
if (nrow(cis)) {
  cis <- cis %>% dplyr::rename(
    `Lower 95% CI` = .data$Lo, `Upper 95% CI` = .data$Hi
  )
}

coefs_with_cis <- coefs %>% 
  dplyr::filter(!stringr::str_detect(index, " Ci ")) %>% 
    dplyr::left_join(cis, by = "index") %>% 
    dplyr::mutate(index = dplyr::if_else(index == "Glb", "Greatest Lower Bound", .data$index)) %>% 
    dplyr::arrange(!stringr::str_detect(index, "Omega")) %>% 
    dplyr::select(Index = .data$index, Estimate = .data$estimate)


pander::pander(coefs_with_cis)

Index	Estimate
Omega	0.7516
Omega Psych Tot	0.7949
Omega Psych H	0.5028
Omega Ordinal	0.8013
Cronbach Alpha	0.7507
Greatest Lower Bound	0.8297
Alpha Ordinal	0.7996

Positive correlations: 45 out of 45 (100%)

Scatter matrix

print(x$scatterMatrix$output$scatterMatrix)

x$scatterMatrix$output$scatterMatrix <- no_md()

Detailed output

print(x)

## 
## Information about this analysis:
## 
##                  Dataframe: res$dat
##                      Items: bf_2R, bf_7, bf_12R, bf_17, bf_22, bf_27R, bf_32, bf_37R, bf_42, bf_45R
##               Observations: 189
##      Positive correlations: 45 out of 45 (100%)
## 
## Estimates assuming interval level:
## 
##              Omega (total): 0.75
##       Omega (hierarchical): 0.5
##    Revelle's omega (total): 0.79
## Greatest Lower Bound (GLB): 0.83
##              Coefficient H: 0.77
##           Cronbach's alpha: 0.75
## Confidence intervals:
##              Omega (total): [0.7, 0.8]
##           Cronbach's alpha: [0.7, 0.8]
## 
## Estimates assuming ordinal level:
## 
##      Ordinal Omega (total): 0.8
##  Ordinal Omega (hierarch.): 0.8
##   Ordinal Cronbach's alpha: 0.8
## Confidence intervals:
##      Ordinal Omega (total): [0.76, 0.84]
##   Ordinal Cronbach's alpha: [0.76, 0.84]
## 
## Note: the normal point estimate and confidence interval for omega are based on the procedure suggested by Dunn, Baguley & Brunsden (2013) using the MBESS function ci.reliability, whereas the psych package point estimate was suggested in Revelle & Zinbarg (2008). See the help ('?scaleStructure') for more information.
## 
## Eigen values: 3.143, 1.385, 0.948, 0.873, 0.818, 0.71, 0.622, 0.561, 0.489, 0.451
## Loadings:
##        TC1    TC2   
## bf_2R          0.617
## bf_7    0.756       
## bf_12R         0.761
## bf_17   0.277  0.344
## bf_22   0.625       
## bf_27R  0.121  0.669
## bf_32   0.762       
## bf_37R  0.412  0.352
## bf_42   0.670       
## bf_45R -0.155  0.677
## 
##                  TC1   TC2
## SS loadings    2.281 2.124
## Proportion Var 0.228 0.212
## Cumulative Var 0.228 0.440
## 
##        vars   n mean   sd median trimmed  mad min max range  skew kurtosis
## bf_2R     1 189 3.51 1.14      4    3.58 1.48   1   5     4 -0.45    -0.66
## bf_7      2 189 4.08 0.90      4    4.19 1.48   1   5     4 -0.82     0.13
## bf_12R    3 189 4.01 1.17      4    4.18 1.48   1   5     4 -1.06     0.16
## bf_17     4 189 3.64 1.04      4    3.70 1.48   1   5     4 -0.46    -0.57
## bf_22     5 189 3.49 1.23      4    3.60 1.48   1   5     4 -0.62    -0.66
## bf_27R    6 189 3.93 1.16      4    4.05 1.48   1   5     4 -0.72    -0.71
## bf_32     7 189 3.94 0.93      4    4.05 1.48   1   5     4 -0.78     0.19
## bf_37R    8 189 4.02 1.06      4    4.16 1.48   1   5     4 -0.84    -0.35
## bf_42     9 189 3.71 1.06      4    3.82 1.48   1   5     4 -0.74    -0.01
## bf_45R   10 189 4.44 0.89      5    4.61 0.00   1   5     4 -1.74     2.82
##          se
## bf_2R  0.08
## bf_7   0.07
## bf_12R 0.09
## bf_17  0.08
## bf_22  0.09
## bf_27R 0.08
## bf_32  0.07
## bf_37R 0.08
## bf_42  0.08
## bf_45R 0.06

Summary statistics

for (i in seq_along(names(items))) {
  attributes(items[[i]]) = recursive_escape(attributes(items[[i]]))
}
escaped_table(codebook_table(items))

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
bf_2R	Big 5 Agreeableness item 2	numeric	5. Disapprove strongly, 4. Disapprove slightly, 3. Neither approve nore disapprove, 2. Approve slightly, 1. Approve strongly	1	192	193	3.51	1.14	1	3	4	4	5	▁▃▁▆▁▇▁▅
bf_7	Big 5 Agreeableness item 7	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	1	192	193	4.09	0.9	1	4	4	5	5	▁▁▁▃▁▇▁▇
bf_12R	Big 5 Agreeableness item 12	numeric	5. Disapprove strongly, 4. Disapprove slightly, 3. Neither approve nore disapprove, 2. Approve slightly, 1. Approve strongly	1	192	193	3.98	1.18	1	3	4	5	5	▁▂▁▂▁▅▁▇
bf_17	Big 5 Agreeableness item 17	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	2	191	193	3.63	1.06	1	3	4	4	5	▁▃▁▅▁▇▁▅
bf_22	Big 5 Agreeableness item 22	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	2	191	193	3.48	1.23	1	3	4	4	5	▂▃▁▃▁▇▁▅
bf_27R	Big 5 Agreeableness item 27	numeric	5. Disapprove strongly, 4. Disapprove slightly, 3. Neither approve nore disapprove, 2. Approve slightly, 1. Approve strongly	1	192	193	3.91	1.17	1	3	4	5	5	▁▃▁▃▁▅▁▇
bf_32	Big 5 Agreeableness item 32	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	3	190	193	3.93	0.93	1	3	4	5	5	▁▂▁▃▁▇▁▅
bf_37R	Big 5 Agreeableness item 37	numeric	5. Disapprove strongly, 4. Disapprove slightly, 3. Neither approve nore disapprove, 2. Approve slightly, 1. Approve strongly	1	192	193	4.02	1.07	1	3	4	5	5	▁▂▁▂▁▆▁▇
bf_42	Big 5 Agreeableness item 42	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	1	192	193	3.7	1.07	1	3	4	4	5	▁▂▁▃▁▇▁▅
bf_45R	Big 5 Agreeableness item 45	numeric	5. Disapprove strongly, 4. Disapprove slightly, 3. Neither approve nore disapprove, 2. Approve slightly, 1. Approve strongly	1	192	193	4.44	0.88	1	4	5	5	5	▁▁▁▁▁▃▁▇

Scale: Conscientiousness

Overview

Reliability: ω_ordinal [95% CI] = 0.21 [0.1;0.31].

Missing: 4.

old_height <- knitr::opts_chunk$get("fig.height")
new_height <- length(scale_info$scale_item_names)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
new_height <- ifelse(is.na(new_height) | is.nan(new_height), 
                     old_height, new_height)
knitr::opts_chunk$set(fig.height = new_height)

if (dplyr::n_distinct(na.omit(unlist(items))) < 12) {
  likert_plot <- likert_from_items(items)
  if (!is.null(likert_plot)) {
    graphics::plot(likert_plot)
  }
}

knitr::opts_chunk$set(fig.height = old_height)

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

dist_plot <- plot_labelled(scale, scale_name, wrap_at)

choices <- attributes(items[[1]])$item$choices
breaks <- as.numeric(names(choices))
if (length(breaks)) {
  suppressMessages( # ignore message about overwriting x axis
  dist_plot <- dist_plot +
        ggplot2::scale_x_continuous("values", 
                                breaks = breaks, 
                                labels = stringr::str_wrap(unlist(choices), ceiling(wrap_at * 0.21))) +
      ggplot2::expand_limits(x = range(breaks)))
  
}

dist_plot

Reliability details

for (i in seq_along(reliabilities)) {
  rel <- reliabilities[[i]]
  cat(knitr::knit_print(rel, indent = paste0(indent, "####")))
}

Reliability Indices

coefs <- x$scaleReliability$output$dat %>% 
  tidyr::gather(index, estimate) %>% 
  dplyr::filter(index != "n.items", index != "n.observations") %>% 
  dplyr::mutate(index = stringr::str_to_title(
    stringr::str_replace_all(index,
      stringr::fixed("."), " ")))

cis <- coefs %>% 
  dplyr::filter(stringr::str_detect(index, " Ci ")) %>% 
  tidyr::separate(index, c("index", "hilo"), sep = " Ci ") %>% 
  tidyr::spread(hilo, estimate)
if (nrow(cis)) {
  cis <- cis %>% dplyr::rename(
    `Lower 95% CI` = .data$Lo, `Upper 95% CI` = .data$Hi
  )
}

coefs_with_cis <- coefs %>% 
  dplyr::filter(!stringr::str_detect(index, " Ci ")) %>% 
    dplyr::left_join(cis, by = "index") %>% 
    dplyr::mutate(index = dplyr::if_else(index == "Glb", "Greatest Lower Bound", .data$index)) %>% 
    dplyr::arrange(!stringr::str_detect(index, "Omega")) %>% 
    dplyr::select(Index = .data$index, Estimate = .data$estimate)


pander::pander(coefs_with_cis)

Index	Estimate
Omega	0.06757
Omega Psych Tot	0.8491
Omega Psych H	0.6316
Omega Ordinal	0.2074
Cronbach Alpha	0.17
Greatest Lower Bound	0.4445
Alpha Ordinal	0.2643

Positive correlations: 16 out of 36 (44%)

Scatter matrix

print(x$scatterMatrix$output$scatterMatrix)

x$scatterMatrix$output$scatterMatrix <- no_md()

Detailed output

print(x)

## 
## Information about this analysis:
## 
##                  Dataframe: res$dat
##                      Items: bf_3, bf_8R, bf_13, bf_18R, bf_23R, bf_28, bf_33, bf_38R, bf_43R
##               Observations: 189
##      Positive correlations: 16 out of 36 (44%)
## 
## Estimates assuming interval level:
## 
##              Omega (total): 0.07
##       Omega (hierarchical): 0.63
##    Revelle's omega (total): 0.85
## Greatest Lower Bound (GLB): 0.44
##              Coefficient H: 0.83
##           Cronbach's alpha: 0.17
## Confidence intervals:
##              Omega (total): [0, 0.15]
##           Cronbach's alpha: [0, 0.34]
## 
## Estimates assuming ordinal level:
## 
##      Ordinal Omega (total): 0.21
##  Ordinal Omega (hierarch.): 0.13
##   Ordinal Cronbach's alpha: 0.26
## Confidence intervals:
##      Ordinal Omega (total): [0.1, 0.31]
##   Ordinal Cronbach's alpha: [0.1, 0.43]
## 
## Note: the normal point estimate and confidence interval for omega are based on the procedure suggested by Dunn, Baguley & Brunsden (2013) using the MBESS function ci.reliability, whereas the psych package point estimate was suggested in Revelle & Zinbarg (2008). See the help ('?scaleStructure') for more information.
## 
## Eigen values: 3.599, 1.296, 0.814, 0.737, 0.637, 0.582, 0.544, 0.407, 0.383
## Loadings:
##        TC1    TC2   
## bf_3    0.504 -0.416
## bf_8R          0.768
## bf_13   0.854  0.224
## bf_18R         0.747
## bf_23R         0.724
## bf_28   0.739 -0.100
## bf_33   0.730 -0.136
## bf_38R  0.518 -0.243
## bf_43R         0.572
## 
##                  TC1   TC2
## SS loadings    2.345 2.310
## Proportion Var 0.261 0.257
## Cumulative Var 0.261 0.517
## 
##        vars   n mean   sd median trimmed  mad min max range  skew kurtosis
## bf_3      1 189 3.50 1.16      4    3.58 1.48   1   5     4 -0.49    -0.53
## bf_8R     2 189 2.87 1.12      3    2.90 1.48   1   5     4 -0.09    -1.00
## bf_13     3 189 4.03 0.86      4    4.10 1.48   1   5     4 -0.77     0.59
## bf_18R    4 189 2.83 1.32      3    2.78 1.48   1   5     4  0.07    -1.17
## bf_23R    5 189 3.06 1.15      3    3.06 1.48   1   5     4  0.05    -0.84
## bf_28     6 189 3.76 1.08      4    3.86 1.48   1   5     4 -0.65    -0.32
## bf_33     7 189 3.70 0.93      4    3.78 1.48   1   5     4 -0.72     0.43
## bf_38R    8 189 3.54 1.05      4    3.61 1.48   1   5     4 -0.61    -0.10
## bf_43R    9 189 3.48 1.25      4    3.59 1.48   1   5     4 -0.49    -0.80
##          se
## bf_3   0.08
## bf_8R  0.08
## bf_13  0.06
## bf_18R 0.10
## bf_23R 0.08
## bf_28  0.08
## bf_33  0.07
## bf_38R 0.08
## bf_43R 0.09

Summary statistics

for (i in seq_along(names(items))) {
  attributes(items[[i]]) = recursive_escape(attributes(items[[i]]))
}
escaped_table(codebook_table(items))

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
bf_3	Big 5 Conscientiousness item 3	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	1	192	193	3.49	1.15	1	3	4	4	5	▂▃▁▆▁▇▁▅
bf_8R	Big 5 Conscientiousness item 8	numeric	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	2	191	193	2.88	1.13	1	2	3	4	5	▃▇▁▇▁▇▁▂
bf_13	Big 5 Conscientiousness item 13	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	2	191	193	4.03	0.85	1	4	4	5	5	▁▁▁▃▁▇▁▆
bf_18R	Big 5 Conscientiousness item 18	numeric	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	2	191	193	2.85	1.33	1	2	3	4	5	▇▇▁▇▁▇▁▅
bf_23R	Big 5 Conscientiousness item 23	numeric	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	2	191	193	3.06	1.17	1	2	3	4	5	▂▆▁▇▁▆▁▃
bf_28	Big 5 Conscientiousness item 28	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	2	191	193	3.77	1.08	1	3	4	5	5	▁▂▁▅▁▇▁▆
bf_33	Big 5 Conscientiousness item 33	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	1	192	193	3.69	0.94	1	3	4	4	5	▁▂▁▃▁▇▁▃
bf_38R	Big 5 Conscientiousness item 38	numeric	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	1	192	193	3.55	1.06	1	3	4	4	5	▁▂▁▅▁▇▁▃
bf_43R	Big 5 Conscientiousness item 43	numeric	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	1	192	193	3.49	1.25	1	3	4	4.25	5	▂▃▁▅▁▇▁▆

Scale: Neuroticism

Overview

Reliability: ω_ordinal [95% CI] = 0.86 [0.83;0.89].

Missing: 2.

old_height <- knitr::opts_chunk$get("fig.height")
new_height <- length(scale_info$scale_item_names)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
new_height <- ifelse(is.na(new_height) | is.nan(new_height), 
                     old_height, new_height)
knitr::opts_chunk$set(fig.height = new_height)

if (dplyr::n_distinct(na.omit(unlist(items))) < 12) {
  likert_plot <- likert_from_items(items)
  if (!is.null(likert_plot)) {
    graphics::plot(likert_plot)
  }
}

knitr::opts_chunk$set(fig.height = old_height)

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

dist_plot <- plot_labelled(scale, scale_name, wrap_at)

choices <- attributes(items[[1]])$item$choices
breaks <- as.numeric(names(choices))
if (length(breaks)) {
  suppressMessages( # ignore message about overwriting x axis
  dist_plot <- dist_plot +
        ggplot2::scale_x_continuous("values", 
                                breaks = breaks, 
                                labels = stringr::str_wrap(unlist(choices), ceiling(wrap_at * 0.21))) +
      ggplot2::expand_limits(x = range(breaks)))
  
}

dist_plot

Reliability details

for (i in seq_along(reliabilities)) {
  rel <- reliabilities[[i]]
  cat(knitr::knit_print(rel, indent = paste0(indent, "####")))
}

Reliability Indices

coefs <- x$scaleReliability$output$dat %>% 
  tidyr::gather(index, estimate) %>% 
  dplyr::filter(index != "n.items", index != "n.observations") %>% 
  dplyr::mutate(index = stringr::str_to_title(
    stringr::str_replace_all(index,
      stringr::fixed("."), " ")))

cis <- coefs %>% 
  dplyr::filter(stringr::str_detect(index, " Ci ")) %>% 
  tidyr::separate(index, c("index", "hilo"), sep = " Ci ") %>% 
  tidyr::spread(hilo, estimate)
if (nrow(cis)) {
  cis <- cis %>% dplyr::rename(
    `Lower 95% CI` = .data$Lo, `Upper 95% CI` = .data$Hi
  )
}

coefs_with_cis <- coefs %>% 
  dplyr::filter(!stringr::str_detect(index, " Ci ")) %>% 
    dplyr::left_join(cis, by = "index") %>% 
    dplyr::mutate(index = dplyr::if_else(index == "Glb", "Greatest Lower Bound", .data$index)) %>% 
    dplyr::arrange(!stringr::str_detect(index, "Omega")) %>% 
    dplyr::select(Index = .data$index, Estimate = .data$estimate)


pander::pander(coefs_with_cis)

Index	Estimate
Omega	0.8435
Omega Psych Tot	0.8778
Omega Psych H	0.6987
Omega Ordinal	0.8626
Cronbach Alpha	0.8365
Greatest Lower Bound	0.8731
Alpha Ordinal	0.8564

Positive correlations: 28 out of 28 (100%)

Scatter matrix

print(x$scatterMatrix$output$scatterMatrix)

x$scatterMatrix$output$scatterMatrix <- no_md()

Detailed output

print(x)

## 
## Information about this analysis:
## 
##                  Dataframe: res$dat
##                      Items: bf_4, bf_9R, bf_14, bf_19, bf_24R, bf_29, bf_34R, bf_39
##               Observations: 191
##      Positive correlations: 28 out of 28 (100%)
## 
## Estimates assuming interval level:
## 
##              Omega (total): 0.84
##       Omega (hierarchical): 0.7
##    Revelle's omega (total): 0.88
## Greatest Lower Bound (GLB): 0.87
##              Coefficient H: 0.91
##           Cronbach's alpha: 0.84
## Confidence intervals:
##              Omega (total): [0.81, 0.88]
##           Cronbach's alpha: [0.8, 0.87]
## 
## Estimates assuming ordinal level:
## 
##      Ordinal Omega (total): 0.86
##  Ordinal Omega (hierarch.): 0.85
##   Ordinal Cronbach's alpha: 0.86
## Confidence intervals:
##      Ordinal Omega (total): [0.83, 0.89]
##   Ordinal Cronbach's alpha: [0.83, 0.89]
## 
## Note: the normal point estimate and confidence interval for omega are based on the procedure suggested by Dunn, Baguley & Brunsden (2013) using the MBESS function ci.reliability, whereas the psych package point estimate was suggested in Revelle & Zinbarg (2008). See the help ('?scaleStructure') for more information.
## 
## Eigen values: 3.865, 1.118, 0.833, 0.656, 0.531, 0.432, 0.345, 0.219
## Loadings:
##        TC1    TC2   
## bf_4    0.862 -0.169
## bf_9R   0.361  0.635
## bf_14   0.653  0.301
## bf_19   0.802       
## bf_24R -0.197  0.807
## bf_29   0.581       
## bf_34R         0.778
## bf_39   0.569  0.475
## 
##                  TC1   TC2
## SS loadings    2.647 2.007
## Proportion Var 0.331 0.251
## Cumulative Var 0.331 0.582
## 
##        vars   n mean   sd median trimmed  mad min max range  skew kurtosis
## bf_4      1 191 2.20 1.25      2    2.04 1.48   1   5     4  0.78    -0.47
## bf_9R     2 191 3.13 1.26      3    3.16 1.48   1   5     4 -0.04    -1.09
## bf_14     3 191 3.23 1.31      4    3.28 1.48   1   5     4 -0.31    -1.10
## bf_19     4 191 3.29 1.31      4    3.37 1.48   1   5     4 -0.41    -1.00
## bf_24R    5 191 3.01 1.20      3    3.01 1.48   1   5     4  0.08    -0.90
## bf_29     6 191 2.94 1.38      3    2.93 1.48   1   5     4 -0.02    -1.28
## bf_34R    7 191 2.88 1.25      3    2.86 1.48   1   5     4  0.20    -0.95
## bf_39     8 191 2.91 1.31      3    2.88 1.48   1   5     4  0.08    -1.21
##          se
## bf_4   0.09
## bf_9R  0.09
## bf_14  0.09
## bf_19  0.09
## bf_24R 0.09
## bf_29  0.10
## bf_34R 0.09
## bf_39  0.09

Summary statistics

for (i in seq_along(names(items))) {
  attributes(items[[i]]) = recursive_escape(attributes(items[[i]]))
}
escaped_table(codebook_table(items))

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
bf_4	Big 5 Neuroticism item 4	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	1	192	193	2.19	1.25	1	1	2	3	5	▇▆▁▃▁▂▁▂
bf_9R	Big 5 Neuroticism item 9	numeric	5. Disapprove strongly, 4. Disapprove slightly, 3. Neither approve nore disapprove, 2. Approve slightly, 1. Approve strongly	1	192	193	3.12	1.27	1	2	3	4	5	▃▇▁▇▁▇▁▆
bf_14	Big 5 Neuroticism item 14	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	2	191	193	3.23	1.31	1	2	4	4	5	▃▅▁▅▁▇▁▅
bf_19	Big 5 Neuroticism item 19	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	2	191	193	3.29	1.31	1	2	4	4	5	▃▃▁▅▁▇▁▅
bf_24R	Big 5 Neuroticism item 24	numeric	5. Disapprove strongly, 4. Disapprove slightly, 3. Neither approve nore disapprove, 2. Approve slightly, 1. Approve strongly	2	191	193	3.01	1.2	1	2	3	4	5	▃▇▁▇▁▆▁▃
bf_29	Big 5 Neuroticism item 29	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	1	192	193	2.93	1.38	1	2	3	4	5	▇▆▁▆▁▇▁▅
bf_34R	Big 5 Neuroticism item 34	numeric	5. Disapprove strongly, 4. Disapprove slightly, 3. Neither approve nore disapprove, 2. Approve slightly, 1. Approve strongly	1	192	193	2.88	1.25	1	2	3	4	5	▃▇▁▇▁▅▁▃
bf_39	Big 5 Neuroticism item 39	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	1	192	193	2.9	1.31	1	2	3	4	5	▅▇▁▅▁▇▁▃

Scale: Openness to experience

Overview

Reliability: ω_ordinal [95% CI] = 0.73 [0.68;0.79].

Missing: 7.

old_height <- knitr::opts_chunk$get("fig.height")
new_height <- length(scale_info$scale_item_names)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
new_height <- ifelse(is.na(new_height) | is.nan(new_height), 
                     old_height, new_height)
knitr::opts_chunk$set(fig.height = new_height)

if (dplyr::n_distinct(na.omit(unlist(items))) < 12) {
  likert_plot <- likert_from_items(items)
  if (!is.null(likert_plot)) {
    graphics::plot(likert_plot)
  }
}

knitr::opts_chunk$set(fig.height = old_height)

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

dist_plot <- plot_labelled(scale, scale_name, wrap_at)

choices <- attributes(items[[1]])$item$choices
breaks <- as.numeric(names(choices))
if (length(breaks)) {
  suppressMessages( # ignore message about overwriting x axis
  dist_plot <- dist_plot +
        ggplot2::scale_x_continuous("values", 
                                breaks = breaks, 
                                labels = stringr::str_wrap(unlist(choices), ceiling(wrap_at * 0.21))) +
      ggplot2::expand_limits(x = range(breaks)))
  
}

dist_plot

Reliability details

for (i in seq_along(reliabilities)) {
  rel <- reliabilities[[i]]
  cat(knitr::knit_print(rel, indent = paste0(indent, "####")))
}

Reliability Indices

coefs <- x$scaleReliability$output$dat %>% 
  tidyr::gather(index, estimate) %>% 
  dplyr::filter(index != "n.items", index != "n.observations") %>% 
  dplyr::mutate(index = stringr::str_to_title(
    stringr::str_replace_all(index,
      stringr::fixed("."), " ")))

cis <- coefs %>% 
  dplyr::filter(stringr::str_detect(index, " Ci ")) %>% 
  tidyr::separate(index, c("index", "hilo"), sep = " Ci ") %>% 
  tidyr::spread(hilo, estimate)
if (nrow(cis)) {
  cis <- cis %>% dplyr::rename(
    `Lower 95% CI` = .data$Lo, `Upper 95% CI` = .data$Hi
  )
}

coefs_with_cis <- coefs %>% 
  dplyr::filter(!stringr::str_detect(index, " Ci ")) %>% 
    dplyr::left_join(cis, by = "index") %>% 
    dplyr::mutate(index = dplyr::if_else(index == "Glb", "Greatest Lower Bound", .data$index)) %>% 
    dplyr::arrange(!stringr::str_detect(index, "Omega")) %>% 
    dplyr::select(Index = .data$index, Estimate = .data$estimate)


pander::pander(coefs_with_cis)

Index	Estimate
Omega	0.6806
Omega Psych Tot	0.8255
Omega Psych H	0.6246
Omega Ordinal	0.7319
Cronbach Alpha	0.6211
Greatest Lower Bound	0.8128
Alpha Ordinal	0.677

Positive correlations: 30 out of 45 (67%)

Scatter matrix

print(x$scatterMatrix$output$scatterMatrix)

x$scatterMatrix$output$scatterMatrix <- no_md()

Detailed output

print(x)

## 
## Information about this analysis:
## 
##                  Dataframe: res$dat
##                      Items: bf_5, bf_10, bf_15, bf_20, bf_25, bf_30, bf_35R, bf_40, bf_41R, bf_44
##               Observations: 186
##      Positive correlations: 30 out of 45 (67%)
## 
## Estimates assuming interval level:
## 
##              Omega (total): 0.68
##       Omega (hierarchical): 0.62
##    Revelle's omega (total): 0.83
## Greatest Lower Bound (GLB): 0.81
##              Coefficient H: 0.88
##           Cronbach's alpha: 0.62
## Confidence intervals:
##              Omega (total): [0.61, 0.75]
##           Cronbach's alpha: [0.55, 0.69]
## 
## Estimates assuming ordinal level:
## 
##      Ordinal Omega (total): 0.73
##  Ordinal Omega (hierarch.): 0.73
##   Ordinal Cronbach's alpha: 0.68
## Confidence intervals:
##      Ordinal Omega (total): [0.68, 0.79]
##   Ordinal Cronbach's alpha: [0.61, 0.74]
## 
## Note: the normal point estimate and confidence interval for omega are based on the procedure suggested by Dunn, Baguley & Brunsden (2013) using the MBESS function ci.reliability, whereas the psych package point estimate was suggested in Revelle & Zinbarg (2008). See the help ('?scaleStructure') for more information.
## 
## Eigen values: 3.519, 1.178, 1.154, 1.07, 0.877, 0.678, 0.587, 0.371, 0.316, 0.249
## Loadings:
##        TC1    TC2    TC3    TC4   
## bf_5    0.873                     
## bf_10          0.667  0.250  0.102
## bf_15   0.253  0.100 -0.171 -0.801
## bf_20   0.889                     
## bf_25   0.843                     
## bf_30   0.404         0.652  0.174
## bf_35R  0.145 -0.797  0.128  0.123
## bf_40   0.153  0.681              
## bf_41R  0.199        -0.568  0.612
## bf_44          0.126  0.752       
## 
##                  TC1   TC2   TC3   TC4
## SS loadings    2.582 1.584 1.431 1.083
## Proportion Var 0.258 0.158 0.143 0.108
## Cumulative Var 0.258 0.417 0.560 0.668
## 
##        vars   n mean   sd median trimmed  mad min max range  skew kurtosis
## bf_5      1 186 3.46 1.01      4    3.49 1.48   1   5     4 -0.34    -0.41
## bf_10     2 186 4.23 0.84      4    4.35 1.48   1   5     4 -1.17     1.62
## bf_15     3 186 2.90 0.94      3    2.93 1.48   1   5     4 -0.11    -0.15
## bf_20     4 186 3.69 1.09      4    3.79 1.48   1   5     4 -0.67    -0.22
## bf_25     5 186 3.55 0.99      4    3.60 1.48   1   5     4 -0.48    -0.29
## bf_30     6 186 3.75 1.24      4    3.90 1.48   1   5     4 -0.77    -0.49
## bf_35R    7 186 2.31 1.18      2    2.19 1.48   1   5     4  0.68    -0.45
## bf_40     8 186 3.97 0.88      4    4.06 0.74   1   5     4 -0.85     0.69
## bf_41R    9 186 2.42 1.47      2    2.29 1.48   1   5     4  0.60    -1.08
## bf_44    10 186 3.30 1.20      3    3.37 1.48   1   5     4 -0.33    -0.81
##          se
## bf_5   0.07
## bf_10  0.06
## bf_15  0.07
## bf_20  0.08
## bf_25  0.07
## bf_30  0.09
## bf_35R 0.09
## bf_40  0.06
## bf_41R 0.11
## bf_44  0.09

Summary statistics

for (i in seq_along(names(items))) {
  attributes(items[[i]]) = recursive_escape(attributes(items[[i]]))
}
escaped_table(codebook_table(items))

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
bf_5	Big 5 Openness to experience item 5	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	2	191	193	3.43	1.02	1	3	4	4	5	▁▃▁▇▁▇▁▃
bf_10	Big 5 Openness to experience item 10	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	1	192	193	4.22	0.84	1	4	4	5	5	▁▁▁▂▁▇▁▇
bf_15	Big 5 Openness to experience item 15	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	3	190	193	2.88	0.94	1	2	3	3	5	▂▃▁▇▁▃▁▁
bf_20	Big 5 Openness to experience item 20	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	2	191	193	3.68	1.08	1	3	4	4	5	▁▂▁▅▁▇▁▅
bf_25	Big 5 Openness to experience item 25	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	3	190	193	3.55	0.98	1	3	4	4	5	▁▂▁▅▁▇▁▃
bf_30	Big 5 Openness to experience item 30	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	2	191	193	3.73	1.26	1	3	4	5	5	▂▃▁▃▁▇▁▇
bf_35R	Big 5 Openness to experience item 35	numeric	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	1	192	193	2.32	1.19	1	1	2	3	5	▇▇▁▅▁▃▁▂
bf_40	Big 5 Openness to experience item 40	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	1	192	193	3.96	0.87	1	4	4	5	5	▁▁▁▂▁▇▁▅
bf_41R	Big 5 Openness to experience item 41	numeric	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	1	192	193	2.41	1.46	1	1	2	4	5	▇▅▁▃▁▂▁▃
bf_44	Big 5 Openness to experience item 44	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	1	192	193	3.26	1.21	1	2	3	4	5	▂▅▁▇▁▇▁▅

Scale: Heteroduperie - Social Desirability

Overview

Reliability: ω_ordinal [95% CI] = 0.84 [0.81;0.88].

Missing: 6.

old_height <- knitr::opts_chunk$get("fig.height")
new_height <- length(scale_info$scale_item_names)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
new_height <- ifelse(is.na(new_height) | is.nan(new_height), 
                     old_height, new_height)
knitr::opts_chunk$set(fig.height = new_height)

if (dplyr::n_distinct(na.omit(unlist(items))) < 12) {
  likert_plot <- likert_from_items(items)
  if (!is.null(likert_plot)) {
    graphics::plot(likert_plot)
  }
}

knitr::opts_chunk$set(fig.height = old_height)

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

dist_plot <- plot_labelled(scale, scale_name, wrap_at)

choices <- attributes(items[[1]])$item$choices
breaks <- as.numeric(names(choices))
if (length(breaks)) {
  suppressMessages( # ignore message about overwriting x axis
  dist_plot <- dist_plot +
        ggplot2::scale_x_continuous("values", 
                                breaks = breaks, 
                                labels = stringr::str_wrap(unlist(choices), ceiling(wrap_at * 0.21))) +
      ggplot2::expand_limits(x = range(breaks)))
  
}

dist_plot

Reliability details

for (i in seq_along(reliabilities)) {
  rel <- reliabilities[[i]]
  cat(knitr::knit_print(rel, indent = paste0(indent, "####")))
}

Reliability Indices

coefs <- x$scaleReliability$output$dat %>% 
  tidyr::gather(index, estimate) %>% 
  dplyr::filter(index != "n.items", index != "n.observations") %>% 
  dplyr::mutate(index = stringr::str_to_title(
    stringr::str_replace_all(index,
      stringr::fixed("."), " ")))

cis <- coefs %>% 
  dplyr::filter(stringr::str_detect(index, " Ci ")) %>% 
  tidyr::separate(index, c("index", "hilo"), sep = " Ci ") %>% 
  tidyr::spread(hilo, estimate)
if (nrow(cis)) {
  cis <- cis %>% dplyr::rename(
    `Lower 95% CI` = .data$Lo, `Upper 95% CI` = .data$Hi
  )
}

coefs_with_cis <- coefs %>% 
  dplyr::filter(!stringr::str_detect(index, " Ci ")) %>% 
    dplyr::left_join(cis, by = "index") %>% 
    dplyr::mutate(index = dplyr::if_else(index == "Glb", "Greatest Lower Bound", .data$index)) %>% 
    dplyr::arrange(!stringr::str_detect(index, "Omega")) %>% 
    dplyr::select(Index = .data$index, Estimate = .data$estimate)


pander::pander(coefs_with_cis)

Index	Estimate
Omega	0.8329
Omega Psych Tot	0.856
Omega Psych H	0.5431
Omega Ordinal	0.8439
Cronbach Alpha	0.8297
Greatest Lower Bound	0.8952
Alpha Ordinal	0.8425

Positive correlations: 152 out of 153 (99%)

Scatter matrix

print(x$scatterMatrix$output$scatterMatrix)

x$scatterMatrix$output$scatterMatrix <- no_md()

Detailed output

print(x)

## 
## Information about this analysis:
## 
##                  Dataframe: res$dat
##                      Items: soc_d_1, soc_d_2R, soc_d_3, soc_d_4R, soc_d_5R, soc_d_6, soc_d_7R, soc_d_8, soc_d_9, soc_d_10R, soc_d_11R, soc_d_12, soc_d_13R, soc_d_14R, soc_d_15, soc_d_16, soc_d_17R, soc_d_18
##               Observations: 187
##      Positive correlations: 152 out of 153 (99%)
## 
## Estimates assuming interval level:
## 
##              Omega (total): 0.83
##       Omega (hierarchical): 0.54
##    Revelle's omega (total): 0.86
## Greatest Lower Bound (GLB): 0.9
##              Coefficient H: 0.84
##           Cronbach's alpha: 0.83
## Confidence intervals:
##              Omega (total): [0.8, 0.87]
##           Cronbach's alpha: [0.79, 0.87]
## 
## Estimates assuming ordinal level:
## 
##      Ordinal Omega (total): 0.84
##  Ordinal Omega (hierarch.): 0.84
##   Ordinal Cronbach's alpha: 0.84
## Confidence intervals:
##      Ordinal Omega (total): [0.81, 0.88]
##   Ordinal Cronbach's alpha: [0.81, 0.88]
## 
## Note: the normal point estimate and confidence interval for omega are based on the procedure suggested by Dunn, Baguley & Brunsden (2013) using the MBESS function ci.reliability, whereas the psych package point estimate was suggested in Revelle & Zinbarg (2008). See the help ('?scaleStructure') for more information.
## 
## Eigen values: 4.742, 1.742, 1.262, 1.127, 1.071, 1.03, 0.863, 0.84, 0.794, 0.674, 0.648, 0.59, 0.565, 0.538, 0.468, 0.463, 0.348, 0.234
## Loadings:
##           TC1    TC3    TC6    TC2    TC4    TC5   
## soc_d_1                                       0.848
## soc_d_2R          0.759        -0.169              
## soc_d_3                                0.880       
## soc_d_4R   0.434  0.227         0.276         0.276
## soc_d_5R          0.635         0.243              
## soc_d_6    0.816         0.165 -0.135              
## soc_d_7R   0.117  0.471         0.360         0.242
## soc_d_8    0.408         0.207 -0.289 -0.232  0.156
## soc_d_9    0.335  0.164        -0.591  0.201       
## soc_d_10R                0.187  0.469  0.510       
## soc_d_11R  0.352  0.143         0.546  0.320       
## soc_d_12   0.282         0.513                     
## soc_d_13R         0.628  0.270  0.187 -0.119 -0.280
## soc_d_14R  0.880                                   
## soc_d_15  -0.123  0.147  0.343 -0.207  0.292  0.462
## soc_d_16          0.212  0.667 -0.139        -0.107
## soc_d_17R         0.597 -0.205 -0.267  0.313  0.156
## soc_d_18                 0.783  0.134              
## 
##                  TC1   TC3   TC6   TC2   TC4   TC5
## SS loadings    2.155 2.150 1.679 1.473 1.460 1.252
## Proportion Var 0.120 0.119 0.093 0.082 0.081 0.070
## Cumulative Var 0.120 0.239 0.332 0.414 0.495 0.565
## 
##           vars   n mean   sd median trimmed  mad min max range  skew
## soc_d_1      1 187 4.12 0.87      4    4.24 1.48   1   5     4 -1.05
## soc_d_2R     2 187 2.84 1.29      3    2.80 1.48   1   5     4  0.37
## soc_d_3      3 187 3.13 1.01      3    3.17 1.48   1   5     4 -0.27
## soc_d_4R     4 187 1.98 1.12      2    1.80 1.48   1   5     4  1.15
## soc_d_5R     5 187 3.26 1.12      3    3.26 1.48   1   5     4 -0.08
## soc_d_6      6 187 3.06 1.25      3    3.07 1.48   1   5     4  0.04
## soc_d_7R     7 187 3.14 1.23      3    3.13 1.48   1   5     4  0.08
## soc_d_8      8 187 3.32 1.33      4    3.39 1.48   1   5     4 -0.23
## soc_d_9      9 187 3.49 1.26      4    3.58 1.48   1   5     4 -0.40
## soc_d_10R   10 187 3.22 1.20      3    3.24 1.48   1   5     4 -0.10
## soc_d_11R   11 187 3.09 1.23      3    3.11 1.48   1   5     4  0.03
## soc_d_12    12 187 3.66 1.10      4    3.75 1.48   1   5     4 -0.65
## soc_d_13R   13 187 3.21 1.35      3    3.26 1.48   1   5     4 -0.11
## soc_d_14R   14 187 2.97 1.31      3    2.96 1.48   1   5     4  0.14
## soc_d_15    15 187 3.64 0.96      4    3.69 1.48   1   5     4 -0.49
## soc_d_16    16 187 3.21 1.13      3    3.26 1.48   1   5     4 -0.33
## soc_d_17R   17 187 3.05 1.25      3    3.06 1.48   1   5     4 -0.02
## soc_d_18    18 187 3.47 1.30      4    3.58 1.48   1   5     4 -0.39
##           kurtosis   se
## soc_d_1       1.11 0.06
## soc_d_2R     -1.02 0.09
## soc_d_3      -0.37 0.07
## soc_d_4R      0.60 0.08
## soc_d_5R     -0.90 0.08
## soc_d_6      -1.14 0.09
## soc_d_7R     -1.20 0.09
## soc_d_8      -1.21 0.10
## soc_d_9      -0.97 0.09
## soc_d_10R    -1.11 0.09
## soc_d_11R    -1.11 0.09
## soc_d_12     -0.31 0.08
## soc_d_13R    -1.24 0.10
## soc_d_14R    -1.24 0.10
## soc_d_15     -0.46 0.07
## soc_d_16     -0.69 0.08
## soc_d_17R    -1.00 0.09
## soc_d_18     -0.95 0.09

Summary statistics

for (i in seq_along(names(items))) {
  attributes(items[[i]]) = recursive_escape(attributes(items[[i]]))
}
escaped_table(codebook_table(items))

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
soc_d_1	Social desirability item 1	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	4	189	193	4.12	0.87	1	4	4	5	5	▁▁▁▂▁▇▁▆
soc_d_2R	Social desirability item 2	numeric	5. Disapprove strongly, 4. Disapprove slightly, 3. Neither approve nore disapprove, 2. Approve slightly, 1. Approve strongly	4	189	193	2.85	1.29	1	2	3	4	5	▃▇▁▅▁▃▁▃
soc_d_3	Social desirability item 3	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	4	189	193	3.13	1.01	1	3	3	4	5	▂▃▁▇▁▆▁▂
soc_d_4R	Social desirability item 4	numeric	5. Disapprove strongly, 4. Disapprove slightly, 3. Neither approve nore disapprove, 2. Approve slightly, 1. Approve strongly	4	189	193	1.98	1.11	1	1	2	2	5	▇▇▁▂▁▁▁▁
soc_d_5R	Social desirability item 5	numeric	5. Disapprove strongly, 4. Disapprove slightly, 3. Neither approve nore disapprove, 2. Approve slightly, 1. Approve strongly	4	189	193	3.25	1.12	1	2	3	4	5	▁▇▁▇▁▇▁▅
soc_d_6	Social desirability item 6	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	4	189	193	3.05	1.25	1	2	3	4	5	▃▇▁▆▁▇▁▃
soc_d_7R	Social desirability item 7	numeric	5. Disapprove strongly, 4. Disapprove slightly, 3. Neither approve nore disapprove, 2. Approve slightly, 1. Approve strongly	4	189	193	3.13	1.23	1	2	3	4	5	▂▇▁▅▁▆▁▅
soc_d_8	Social desirability item 8	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	4	189	193	3.31	1.33	1	2	4	4	5	▃▇▁▅▁▇▁▇
soc_d_9	Social desirability item 9	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	4	189	193	3.48	1.26	1	2	4	5	5	▂▅▁▆▁▇▁▇
soc_d_10R	Social desirability item 10	numeric	5. Disapprove strongly, 4. Disapprove slightly, 3. Neither approve nore disapprove, 2. Approve slightly, 1. Approve strongly	4	189	193	3.21	1.2	1	2	3	4	5	▂▇▁▅▁▇▁▅
soc_d_11R	Social desirability item 11	numeric	5. Disapprove strongly, 4. Disapprove slightly, 3. Neither approve nore disapprove, 2. Approve slightly, 1. Approve strongly	4	189	193	3.08	1.23	1	2	3	4	5	▂▇▁▆▁▇▁▅
soc_d_12	Social desirability item 12	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	4	189	193	3.65	1.1	1	3	4	4	5	▁▂▁▅▁▇▁▅
soc_d_13R	Social desirability item 13	numeric	5. Disapprove strongly, 4. Disapprove slightly, 3. Neither approve nore disapprove, 2. Approve slightly, 1. Approve strongly	2	191	193	3.2	1.35	1	2	3	4	5	▅▇▁▇▁▇▁▇
soc_d_14R	Social desirability item 14	numeric	5. Disapprove strongly, 4. Disapprove slightly, 3. Neither approve nore disapprove, 2. Approve slightly, 1. Approve strongly	2	191	193	2.96	1.3	1	2	3	4	5	▃▇▁▃▁▆▁▃
soc_d_15	Social desirability item 15	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	2	191	193	3.64	0.97	1	3	4	4	5	▁▂▁▃▁▇▁▃
soc_d_16	Social desirability item 16	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	2	191	193	3.22	1.14	1	2	3	4	5	▂▅▁▆▁▇▁▃
soc_d_17R	Social desirability item 17	numeric	5. Disapprove strongly, 4. Disapprove slightly, 3. Neither approve nore disapprove, 2. Approve slightly, 1. Approve strongly	2	191	193	3.03	1.25	1	2	3	4	5	▃▆▁▇▁▆▁▅
soc_d_18	Social desirability item 18	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	4	189	193	3.47	1.3	1	3	4	5	5	▂▃▁▇▁▆▁▇

missingness_report

Missingness report

if (length(md_pattern)) {
  if (knitr::is_html_output()) {
    rmarkdown::paged_table(md_pattern, options = list(rows.print = 10))
  } else {
    knitr::kable(md_pattern)
  }
}

items

Codebook table

export_table(metadata_table)

jsonld

JSON-LD metadata

The following JSON-LD can be found by search engines, if you share this codebook publicly on the web.

{
  "name": "French-Belgian Student data on Big 5, Social Desirability as measured by Heteroduperie and Anchoring Paradigm, entire data set",
  "description": "45 items taking from the french translation of the Big 5 Personality questionnaire (Plaisant et al. 2010), 18 items from the subscale 'Hétéroduperie' of the french social desirability scale (Tournois et al., 2010) and 3 Anchoring paradigm items as used in the ManyLabs replication project (Klein et al., 2014). Also includes 7 careless response items based on Meade and Craig (2012)\n\n\n## Table of variables\nThis table contains variable names, labels, their central tendencies and other attributes.\n\n|name                                |label                                                                                                                       |data_type |value_labels                                                                                                                       |scale_item_names                                                                                                                                                                   |missing |complete |n   |empty |n_unique |min |max |mean     |sd        |p0    |p25  |p50   |p75   |p100    |hist     |\n|:-----------------------------------|:---------------------------------------------------------------------------------------------------------------------------|:---------|:----------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------|:--------|:---|:-----|:--------|:---|:---|:--------|:---------|:-----|:----|:-----|:-----|:-------|:--------|\n|V1                                  |NA                                                                                                                          |integer   |NA                                                                                                                                 |NA                                                                                                                                                                                 |0       |193      |193 |NA    |NA       |NA  |NA  |97       |55.86     |1     |49   |97    |145   |193     |▇▇▇▇▇▇▇▇ |\n|id                                  |NA                                                                                                                          |character |NA                                                                                                                                 |NA                                                                                                                                                                                 |0       |193      |193 |0     |193      |4   |5   |NA       |NA        |NA    |NA   |NA    |NA    |NA      |NA       |\n|participant                         |NA                                                                                                                          |character |NA                                                                                                                                 |NA                                                                                                                                                                                 |0       |193      |193 |0     |193      |4   |5   |NA       |NA        |NA    |NA   |NA    |NA    |NA      |NA       |\n|gender                              |Reported gender information                                                                                                 |integer   |1. Female, - 2. Male                                                                                                                 |NA                                                                                                                                                                                 |1       |192      |193 |NA    |NA       |NA  |NA  |1.35     |0.48      |1     |1    |1     |2     |2       |▇▁▁▁▁▁▁▅ |\n|acad_status                         |Academic status                                                                                                             |integer   |0. other, - 1. bachelor, - 2. master, - 3. PhD, - 4. other                                                                                 |NA                                                                                                                                                                                 |1       |192      |193 |NA    |NA       |NA  |NA  |1.22     |0.52      |0     |1    |1     |2     |3       |▁▁▇▁▁▃▁▁ |\n|age                                 |Age Group                                                                                                                   |integer   |1. 18-29 yo, - 2. 21-25 yo, - 3. 26-30 yo, - 4. 31-35 yo, - 5. 36-40 yo, - 6. 41-50 yo, - 7. 51-60 yo, - 8. 61-70 yo, - 9. 71-80 yo, - 10. 81-110 yo |NA                                                                                                                                                                                 |1       |192      |193 |NA    |NA       |NA  |NA  |1.66     |0.8       |1     |1    |2     |2     |7       |▇▆▂▁▁▁▁▁ |\n|refused                             |Refused participation after consent                                                                                         |integer   |0. No, - 1. Yes                                                                                                                      |NA                                                                                                                                                                                 |0       |193      |193 |NA    |NA       |NA  |NA  |0.0052   |0.072     |0     |0    |0     |0     |1       |▇▁▁▁▁▁▁▁ |\n|reason                              |Reason for refusal                                                                                                          |integer   |0. Not refused, - 1. Data sharing, - 2. Not enough time, - 3. Other                                                                      |NA                                                                                                                                                                                 |0       |193      |193 |NA    |NA       |NA  |NA  |0.0052   |0.072     |0     |0    |0     |0     |1       |▇▁▁▁▁▁▁▁ |\n|consent                             |Data sharing policy in consent                                                                                              |character |A. Data will be shared, - B. Data will not be shared                                                                                 |NA                                                                                                                                                                                 |0       |193      |193 |0     |2        |1   |1   |NA       |NA        |NA    |NA   |NA    |NA    |NA      |NA       |\n|bf_1                                |Big 5 Extraversion item 1                                                                                                   |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |2       |191      |193 |NA    |NA       |NA  |NA  |3.57     |1.19      |1     |3    |4     |5     |5       |▂▃▁▆▁▇▁▇ |\n|bf_2R                               |Big 5 Agreeableness item 2                                                                                                  |numeric   |5. Disapprove strongly, - 4. Disapprove slightly, - 3. Neither approve nore disapprove, - 2. Approve slightly, - 1. Approve strongly       |NA                                                                                                                                                                                 |1       |192      |193 |NA    |NA       |NA  |NA  |3.51     |1.14      |1     |3    |4     |4     |5       |▁▃▁▆▁▇▁▅ |\n|bf_3                                |Big 5 Conscientiousness item 3                                                                                              |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |1       |192      |193 |NA    |NA       |NA  |NA  |3.49     |1.15      |1     |3    |4     |4     |5       |▂▃▁▆▁▇▁▅ |\n|bf_4                                |Big 5 Neuroticism item 4                                                                                                    |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |1       |192      |193 |NA    |NA       |NA  |NA  |2.19     |1.25      |1     |1    |2     |3     |5       |▇▆▁▃▁▂▁▂ |\n|bf_5                                |Big 5 Openness to experience item 5                                                                                         |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |2       |191      |193 |NA    |NA       |NA  |NA  |3.43     |1.02      |1     |3    |4     |4     |5       |▁▃▁▇▁▇▁▃ |\n|bf_6R                               |Big 5 Extraversion item 6                                                                                                   |numeric   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |1       |192      |193 |NA    |NA       |NA  |NA  |3.06     |1.34      |1     |2    |3     |4     |5       |▅▅▁▆▁▇▁▅ |\n|bf_7                                |Big 5 Agreeableness item 7                                                                                                  |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |1       |192      |193 |NA    |NA       |NA  |NA  |4.09     |0.9       |1     |4    |4     |5     |5       |▁▁▁▃▁▇▁▇ |\n|bf_8R                               |Big 5 Conscientiousness item 8                                                                                              |numeric   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |2       |191      |193 |NA    |NA       |NA  |NA  |2.88     |1.13      |1     |2    |3     |4     |5       |▃▇▁▇▁▇▁▂ |\n|bf_9R                               |Big 5 Neuroticism item 9                                                                                                    |numeric   |5. Disapprove strongly, - 4. Disapprove slightly, - 3. Neither approve nore disapprove, - 2. Approve slightly, - 1. Approve strongly       |NA                                                                                                                                                                                 |1       |192      |193 |NA    |NA       |NA  |NA  |3.12     |1.27      |1     |2    |3     |4     |5       |▃▇▁▇▁▇▁▆ |\n|bf_10                               |Big 5 Openness to experience item 10                                                                                        |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |1       |192      |193 |NA    |NA       |NA  |NA  |4.22     |0.84      |1     |4    |4     |5     |5       |▁▁▁▂▁▇▁▇ |\n|bf_11                               |Big 5 Extraversion item 11                                                                                                  |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |1       |192      |193 |NA    |NA       |NA  |NA  |3.62     |1.06      |1     |3    |4     |4     |5       |▁▃▁▆▁▇▁▅ |\n|bf_12R                              |Big 5 Agreeableness item 12                                                                                                 |numeric   |5. Disapprove strongly, - 4. Disapprove slightly, - 3. Neither approve nore disapprove, - 2. Approve slightly, - 1. Approve strongly       |NA                                                                                                                                                                                 |1       |192      |193 |NA    |NA       |NA  |NA  |3.98     |1.18      |1     |3    |4     |5     |5       |▁▂▁▂▁▅▁▇ |\n|bf_13                               |Big 5 Conscientiousness item 13                                                                                             |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |2       |191      |193 |NA    |NA       |NA  |NA  |4.03     |0.85      |1     |4    |4     |5     |5       |▁▁▁▃▁▇▁▆ |\n|bf_14                               |Big 5 Neuroticism item 14                                                                                                   |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |2       |191      |193 |NA    |NA       |NA  |NA  |3.23     |1.31      |1     |2    |4     |4     |5       |▃▅▁▅▁▇▁▅ |\n|bf_15                               |Big 5 Openness to experience item 15                                                                                        |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |3       |190      |193 |NA    |NA       |NA  |NA  |2.88     |0.94      |1     |2    |3     |3     |5       |▂▃▁▇▁▃▁▁ |\n|bf_16                               |Big 5 Extraversion item 16                                                                                                  |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |2       |191      |193 |NA    |NA       |NA  |NA  |3.68     |1.11      |1     |3    |4     |5     |5       |▁▃▁▃▁▇▁▆ |\n|bf_17                               |Big 5 Agreeableness item 17                                                                                                 |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |2       |191      |193 |NA    |NA       |NA  |NA  |3.63     |1.06      |1     |3    |4     |4     |5       |▁▃▁▅▁▇▁▅ |\n|bf_18R                              |Big 5 Conscientiousness item 18                                                                                             |numeric   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |2       |191      |193 |NA    |NA       |NA  |NA  |2.85     |1.33      |1     |2    |3     |4     |5       |▇▇▁▇▁▇▁▅ |\n|bf_19                               |Big 5 Neuroticism item 19                                                                                                   |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |2       |191      |193 |NA    |NA       |NA  |NA  |3.29     |1.31      |1     |2    |4     |4     |5       |▃▃▁▅▁▇▁▅ |\n|bf_20                               |Big 5 Openness to experience item 20                                                                                        |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |2       |191      |193 |NA    |NA       |NA  |NA  |3.68     |1.08      |1     |3    |4     |4     |5       |▁▂▁▅▁▇▁▅ |\n|bf_21R                              |Big 5 Extraversion item 21                                                                                                  |numeric   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |3       |190      |193 |NA    |NA       |NA  |NA  |2.72     |1.32      |1     |1    |3     |4     |5       |▇▅▁▆▁▇▁▂ |\n|bf_22                               |Big 5 Agreeableness item 22                                                                                                 |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |2       |191      |193 |NA    |NA       |NA  |NA  |3.48     |1.23      |1     |3    |4     |4     |5       |▂▃▁▃▁▇▁▅ |\n|bf_23R                              |Big 5 Conscientiousness item 23                                                                                             |numeric   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |2       |191      |193 |NA    |NA       |NA  |NA  |3.06     |1.17      |1     |2    |3     |4     |5       |▂▆▁▇▁▆▁▃ |\n|bf_24R                              |Big 5 Neuroticism item 24                                                                                                   |numeric   |5. Disapprove strongly, - 4. Disapprove slightly, - 3. Neither approve nore disapprove, - 2. Approve slightly, - 1. Approve strongly       |NA                                                                                                                                                                                 |2       |191      |193 |NA    |NA       |NA  |NA  |3.01     |1.2       |1     |2    |3     |4     |5       |▃▇▁▇▁▆▁▃ |\n|bf_25                               |Big 5 Openness to experience item 25                                                                                        |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |3       |190      |193 |NA    |NA       |NA  |NA  |3.55     |0.98      |1     |3    |4     |4     |5       |▁▂▁▅▁▇▁▃ |\n|bf_26                               |Big 5 Extraversion item 26                                                                                                  |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |1       |192      |193 |NA    |NA       |NA  |NA  |3.41     |1.13      |1     |3    |4     |4     |5       |▂▃▁▆▁▇▁▃ |\n|bf_27R                              |Big 5 Agreeableness item 27                                                                                                 |numeric   |5. Disapprove strongly, - 4. Disapprove slightly, - 3. Neither approve nore disapprove, - 2. Approve slightly, - 1. Approve strongly       |NA                                                                                                                                                                                 |1       |192      |193 |NA    |NA       |NA  |NA  |3.91     |1.17      |1     |3    |4     |5     |5       |▁▃▁▃▁▅▁▇ |\n|bf_28                               |Big 5 Conscientiousness item 28                                                                                             |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |2       |191      |193 |NA    |NA       |NA  |NA  |3.77     |1.08      |1     |3    |4     |5     |5       |▁▂▁▅▁▇▁▆ |\n|bf_29                               |Big 5 Neuroticism item 29                                                                                                   |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |1       |192      |193 |NA    |NA       |NA  |NA  |2.93     |1.38      |1     |2    |3     |4     |5       |▇▆▁▆▁▇▁▅ |\n|bf_30                               |Big 5 Openness to experience item 30                                                                                        |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |2       |191      |193 |NA    |NA       |NA  |NA  |3.73     |1.26      |1     |3    |4     |5     |5       |▂▃▁▃▁▇▁▇ |\n|bf_31R                              |Big 5 Extraversion item 31                                                                                                  |numeric   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |1       |192      |193 |NA    |NA       |NA  |NA  |2.99     |1.33      |1     |2    |3     |4     |5       |▅▅▁▆▁▇▁▃ |\n|bf_32                               |Big 5 Agreeableness item 32                                                                                                 |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |3       |190      |193 |NA    |NA       |NA  |NA  |3.93     |0.93      |1     |3    |4     |5     |5       |▁▂▁▃▁▇▁▅ |\n|bf_33                               |Big 5 Conscientiousness item 33                                                                                             |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |1       |192      |193 |NA    |NA       |NA  |NA  |3.69     |0.94      |1     |3    |4     |4     |5       |▁▂▁▃▁▇▁▃ |\n|bf_34R                              |Big 5 Neuroticism item 34                                                                                                   |numeric   |5. Disapprove strongly, - 4. Disapprove slightly, - 3. Neither approve nore disapprove, - 2. Approve slightly, - 1. Approve strongly       |NA                                                                                                                                                                                 |1       |192      |193 |NA    |NA       |NA  |NA  |2.88     |1.25      |1     |2    |3     |4     |5       |▃▇▁▇▁▅▁▃ |\n|bf_35R                              |Big 5 Openness to experience item 35                                                                                        |numeric   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |1       |192      |193 |NA    |NA       |NA  |NA  |2.32     |1.19      |1     |1    |2     |3     |5       |▇▇▁▅▁▃▁▂ |\n|bf_36                               |Big 5 Extraversion item 36                                                                                                  |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |2       |191      |193 |NA    |NA       |NA  |NA  |3.27     |1.26      |1     |2    |3     |4     |5       |▃▅▁▇▁▆▁▆ |\n|bf_37R                              |Big 5 Agreeableness item 37                                                                                                 |numeric   |5. Disapprove strongly, - 4. Disapprove slightly, - 3. Neither approve nore disapprove, - 2. Approve slightly, - 1. Approve strongly       |NA                                                                                                                                                                                 |1       |192      |193 |NA    |NA       |NA  |NA  |4.02     |1.07      |1     |3    |4     |5     |5       |▁▂▁▂▁▆▁▇ |\n|bf_38R                              |Big 5 Conscientiousness item 38                                                                                             |numeric   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |1       |192      |193 |NA    |NA       |NA  |NA  |3.55     |1.06      |1     |3    |4     |4     |5       |▁▂▁▅▁▇▁▃ |\n|bf_39                               |Big 5 Neuroticism item 39                                                                                                   |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |1       |192      |193 |NA    |NA       |NA  |NA  |2.9      |1.31      |1     |2    |3     |4     |5       |▅▇▁▅▁▇▁▃ |\n|bf_40                               |Big 5 Openness to experience item 40                                                                                        |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |1       |192      |193 |NA    |NA       |NA  |NA  |3.96     |0.87      |1     |4    |4     |5     |5       |▁▁▁▂▁▇▁▅ |\n|bf_41R                              |Big 5 Openness to experience item 41                                                                                        |numeric   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |1       |192      |193 |NA    |NA       |NA  |NA  |2.41     |1.46      |1     |1    |2     |4     |5       |▇▅▁▃▁▂▁▃ |\n|bf_42                               |Big 5 Agreeableness item 42                                                                                                 |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |1       |192      |193 |NA    |NA       |NA  |NA  |3.7      |1.07      |1     |3    |4     |4     |5       |▁▂▁▃▁▇▁▅ |\n|bf_43R                              |Big 5 Conscientiousness item 43                                                                                             |numeric   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |1       |192      |193 |NA    |NA       |NA  |NA  |3.49     |1.25      |1     |3    |4     |4.25  |5       |▂▃▁▅▁▇▁▆ |\n|bf_44                               |Big 5 Openness to experience item 44                                                                                        |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |1       |192      |193 |NA    |NA       |NA  |NA  |3.26     |1.21      |1     |2    |3     |4     |5       |▂▅▁▇▁▇▁▅ |\n|bf_45R                              |Big 5 Agreeableness item 45                                                                                                 |numeric   |5. Disapprove strongly, - 4. Disapprove slightly, - 3. Neither approve nore disapprove, - 2. Approve slightly, - 1. Approve strongly       |NA                                                                                                                                                                                 |1       |192      |193 |NA    |NA       |NA  |NA  |4.44     |0.88      |1     |4    |5     |5     |5       |▁▁▁▁▁▃▁▇ |\n|cr_1                                |Careless response item 1                                                                                                    |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |2       |191      |193 |NA    |NA       |NA  |NA  |1.23     |0.64      |1     |1    |1     |1     |5       |▇▁▁▁▁▁▁▁ |\n|cr_2                                |Careless response item 2                                                                                                    |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |4       |189      |193 |NA    |NA       |NA  |NA  |1.69     |1.15      |1     |1    |1     |2     |5       |▇▁▁▂▁▁▁▁ |\n|cr_3                                |Careless response item 3                                                                                                    |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |3       |190      |193 |NA    |NA       |NA  |NA  |1.69     |1.14      |1     |1    |1     |2     |5       |▇▁▁▂▁▁▁▁ |\n|cr_4                                |Careless response item 4                                                                                                    |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |1       |192      |193 |NA    |NA       |NA  |NA  |1.2      |0.62      |1     |1    |1     |1     |5       |▇▁▁▁▁▁▁▁ |\n|cr_5                                |Careless response item 5                                                                                                    |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |1       |192      |193 |NA    |NA       |NA  |NA  |1.15     |0.58      |1     |1    |1     |1     |5       |▇▁▁▁▁▁▁▁ |\n|cr_6                                |Careless response item 6                                                                                                    |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |4       |189      |193 |NA    |NA       |NA  |NA  |1.57     |1.28      |1     |1    |1     |1     |5       |▇▁▁▁▁▁▁▁ |\n|cr_7                                |Careless response item 7                                                                                                    |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |4       |189      |193 |NA    |NA       |NA  |NA  |1.48     |1.17      |1     |1    |1     |1     |5       |▇▁▁▁▁▁▁▁ |\n|soc_d_1                             |Social desirability item 1                                                                                                  |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |4       |189      |193 |NA    |NA       |NA  |NA  |4.12     |0.87      |1     |4    |4     |5     |5       |▁▁▁▂▁▇▁▆ |\n|soc_d_2R                            |Social desirability item 2                                                                                                  |numeric   |5. Disapprove strongly, - 4. Disapprove slightly, - 3. Neither approve nore disapprove, - 2. Approve slightly, - 1. Approve strongly       |NA                                                                                                                                                                                 |4       |189      |193 |NA    |NA       |NA  |NA  |2.85     |1.29      |1     |2    |3     |4     |5       |▃▇▁▅▁▃▁▃ |\n|soc_d_3                             |Social desirability item 3                                                                                                  |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |4       |189      |193 |NA    |NA       |NA  |NA  |3.13     |1.01      |1     |3    |3     |4     |5       |▂▃▁▇▁▆▁▂ |\n|soc_d_4R                            |Social desirability item 4                                                                                                  |numeric   |5. Disapprove strongly, - 4. Disapprove slightly, - 3. Neither approve nore disapprove, - 2. Approve slightly, - 1. Approve strongly       |NA                                                                                                                                                                                 |4       |189      |193 |NA    |NA       |NA  |NA  |1.98     |1.11      |1     |1    |2     |2     |5       |▇▇▁▂▁▁▁▁ |\n|soc_d_5R                            |Social desirability item 5                                                                                                  |numeric   |5. Disapprove strongly, - 4. Disapprove slightly, - 3. Neither approve nore disapprove, - 2. Approve slightly, - 1. Approve strongly       |NA                                                                                                                                                                                 |4       |189      |193 |NA    |NA       |NA  |NA  |3.25     |1.12      |1     |2    |3     |4     |5       |▁▇▁▇▁▇▁▅ |\n|soc_d_6                             |Social desirability item 6                                                                                                  |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |4       |189      |193 |NA    |NA       |NA  |NA  |3.05     |1.25      |1     |2    |3     |4     |5       |▃▇▁▆▁▇▁▃ |\n|soc_d_7R                            |Social desirability item 7                                                                                                  |numeric   |5. Disapprove strongly, - 4. Disapprove slightly, - 3. Neither approve nore disapprove, - 2. Approve slightly, - 1. Approve strongly       |NA                                                                                                                                                                                 |4       |189      |193 |NA    |NA       |NA  |NA  |3.13     |1.23      |1     |2    |3     |4     |5       |▂▇▁▅▁▆▁▅ |\n|soc_d_8                             |Social desirability item 8                                                                                                  |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |4       |189      |193 |NA    |NA       |NA  |NA  |3.31     |1.33      |1     |2    |4     |4     |5       |▃▇▁▅▁▇▁▇ |\n|soc_d_9                             |Social desirability item 9                                                                                                  |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |4       |189      |193 |NA    |NA       |NA  |NA  |3.48     |1.26      |1     |2    |4     |5     |5       |▂▅▁▆▁▇▁▇ |\n|soc_d_10R                           |Social desirability item 10                                                                                                 |numeric   |5. Disapprove strongly, - 4. Disapprove slightly, - 3. Neither approve nore disapprove, - 2. Approve slightly, - 1. Approve strongly       |NA                                                                                                                                                                                 |4       |189      |193 |NA    |NA       |NA  |NA  |3.21     |1.2       |1     |2    |3     |4     |5       |▂▇▁▅▁▇▁▅ |\n|soc_d_11R                           |Social desirability item 11                                                                                                 |numeric   |5. Disapprove strongly, - 4. Disapprove slightly, - 3. Neither approve nore disapprove, - 2. Approve slightly, - 1. Approve strongly       |NA                                                                                                                                                                                 |4       |189      |193 |NA    |NA       |NA  |NA  |3.08     |1.23      |1     |2    |3     |4     |5       |▂▇▁▆▁▇▁▅ |\n|soc_d_12                            |Social desirability item 12                                                                                                 |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |4       |189      |193 |NA    |NA       |NA  |NA  |3.65     |1.1       |1     |3    |4     |4     |5       |▁▂▁▅▁▇▁▅ |\n|soc_d_13R                           |Social desirability item 13                                                                                                 |numeric   |5. Disapprove strongly, - 4. Disapprove slightly, - 3. Neither approve nore disapprove, - 2. Approve slightly, - 1. Approve strongly       |NA                                                                                                                                                                                 |2       |191      |193 |NA    |NA       |NA  |NA  |3.2      |1.35      |1     |2    |3     |4     |5       |▅▇▁▇▁▇▁▇ |\n|soc_d_14R                           |Social desirability item 14                                                                                                 |numeric   |5. Disapprove strongly, - 4. Disapprove slightly, - 3. Neither approve nore disapprove, - 2. Approve slightly, - 1. Approve strongly       |NA                                                                                                                                                                                 |2       |191      |193 |NA    |NA       |NA  |NA  |2.96     |1.3       |1     |2    |3     |4     |5       |▃▇▁▃▁▆▁▃ |\n|soc_d_15                            |Social desirability item 15                                                                                                 |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |2       |191      |193 |NA    |NA       |NA  |NA  |3.64     |0.97      |1     |3    |4     |4     |5       |▁▂▁▃▁▇▁▃ |\n|soc_d_16                            |Social desirability item 16                                                                                                 |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |2       |191      |193 |NA    |NA       |NA  |NA  |3.22     |1.14      |1     |2    |3     |4     |5       |▂▅▁▆▁▇▁▃ |\n|soc_d_17R                           |Social desirability item 17                                                                                                 |numeric   |5. Disapprove strongly, - 4. Disapprove slightly, - 3. Neither approve nore disapprove, - 2. Approve slightly, - 1. Approve strongly       |NA                                                                                                                                                                                 |2       |191      |193 |NA    |NA       |NA  |NA  |3.03     |1.25      |1     |2    |3     |4     |5       |▃▆▁▇▁▆▁▅ |\n|soc_d_18                            |Social desirability item 18                                                                                                 |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |4       |189      |193 |NA    |NA       |NA  |NA  |3.47     |1.3       |1     |3    |4     |5     |5       |▂▃▁▇▁▆▁▇ |\n|cond_anc                            |Anchoring condition                                                                                                         |integer   |0. Low anchoring value, - 1. High anchoring value                                                                                    |NA                                                                                                                                                                                 |1       |192      |193 |NA    |NA       |NA  |NA  |0.52     |0.5       |0     |0    |1     |1     |1       |▇▁▁▁▁▁▁▇ |\n|anc_everest                         |Anchoring: How high is Mount Everest                                                                                        |integer   |NA                                                                                                                                 |NA                                                                                                                                                                                 |12      |181      |193 |NA    |NA       |NA  |NA  |28134.86 |259715.18 |600   |3200 |8000  |11000 |3500000 |▇▁▁▁▁▁▁▁ |\n|anc_chicago                         |Anchoring: Population of Chicagor                                                                                           |numeric   |NA                                                                                                                                 |NA                                                                                                                                                                                 |12      |181      |193 |NA    |NA       |NA  |NA  |19.92    |185.67    |0.004 |1.5  |3.5   |7     |2500    |▇▁▁▁▁▁▁▁ |\n|anc_bebe                            |Anchoring: Babies born in US                                                                                                |integer   |NA                                                                                                                                 |NA                                                                                                                                                                                 |13      |180      |193 |NA    |NA       |NA  |NA  |68125.47 |163124.78 |40    |1000 |20000 |50000 |1e+06   |▇▁▁▁▁▁▁▁ |\n|mc_1                                |Manipulation check, question 1: 'Do you remember the consent you signed in the beginning?'                                  |integer   |1. Yes, - 0. No                                                                                                                      |NA                                                                                                                                                                                 |4       |189      |193 |NA    |NA       |NA  |NA  |0.98     |0.14      |0     |1    |1     |1     |1       |▁▁▁▁▁▁▁▇ |\n|mc_2                                |Manipulation check, question 2: 'Do you remember if the consent contained the topic of sharing anonymous data with others?' |integer   |1. Yes, - 0. No                                                                                                                      |NA                                                                                                                                                                                 |3       |190      |193 |NA    |NA       |NA  |NA  |0.74     |0.44      |0     |0    |1     |1     |1       |▃▁▁▁▁▁▁▇ |\n|mc_3                                |Manipulation check, question: 'Will your anonymous data be shared with others?'                                             |integer   |1. Yes, - 0. No                                                                                                                      |NA                                                                                                                                                                                 |5       |188      |193 |NA    |NA       |NA  |NA  |0.56     |0.5       |0     |0    |1     |1     |1       |▆▁▁▁▁▁▁▇ |\n|remarks                             |Observer remarks                                                                                                            |character |NA                                                                                                                                 |NA                                                                                                                                                                                 |0       |193      |193 |168   |26       |0   |95  |NA       |NA        |NA    |NA   |NA    |NA    |NA      |NA       |\n|Extraversion                        |8 bf items aggregated by rowMeans                                                                                           |numeric   |NA                                                                                                                                 |bf_1, bf_6R, bf_11, bf_16, bf_21R, bf_26, bf_31R, bf_36                                                                                                                            |5       |188      |193 |NA    |NA       |NA  |NA  |3.34     |0.92      |1.12  |2.62 |3.25  |4     |5       |▁▂▇▇▆▇▆▅ |\n|Agreeableness                       |10 bf items aggregated by rowMeans                                                                                          |numeric   |NA                                                                                                                                 |bf_2R, bf_7, bf_12R, bf_17, bf_22, bf_27R, bf_32, bf_37R, bf_42, bf_45R                                                                                                            |4       |189      |193 |NA    |NA       |NA  |NA  |3.88     |0.59      |1.9   |3.5  |3.9   |4.3   |5       |▁▁▂▅▇▇▇▂ |\n|Conscientiousness                   |9 bf items aggregated by rowMeans                                                                                           |numeric   |NA                                                                                                                                 |bf_3, bf_8R, bf_13, bf_18R, bf_23R, bf_28, bf_33, bf_38R, bf_43R                                                                                                                   |4       |189      |193 |NA    |NA       |NA  |NA  |3.25     |0.58      |1.78  |2.89 |3.22  |3.56  |4.56    |▂▂▃▇▇▅▃▂ |\n|Neuroticism                         |8 bf items aggregated by rowMeans                                                                                           |numeric   |NA                                                                                                                                 |bf_4, bf_9R, bf_14, bf_19, bf_24R, bf_29, bf_34R, bf_39                                                                                                                            |2       |191      |193 |NA    |NA       |NA  |NA  |2.95     |0.88      |1.25  |2.12 |2.88  |3.62  |4.75    |▂▇▃▇▆▅▃▃ |\n|Openness to experience              |10 bf items aggregated by rowMeans                                                                                          |numeric   |NA                                                                                                                                 |bf_5, bf_10, bf_15, bf_20, bf_25, bf_30, bf_35R, bf_40, bf_41R, bf_44                                                                                                              |7       |186      |193 |NA    |NA       |NA  |NA  |3.61     |0.6       |1.9   |3.2  |3.7   |4     |4.9     |▁▂▃▆▅▇▅▁ |\n|Heteroduperie - Social Desirability |18 soc_d items aggregated by rowMeans                                                                                       |numeric   |NA                                                                                                                                 |soc_d_1, soc_d_2R, soc_d_3, soc_d_4R, soc_d_5R, soc_d_6, soc_d_7R, soc_d_8, soc_d_9, soc_d_10R, soc_d_11R, soc_d_12, soc_d_13R, soc_d_14R, soc_d_15, soc_d_16, soc_d_17R, soc_d_18 |6       |187      |193 |NA    |NA       |NA  |NA  |3.21     |0.6       |1.78  |2.81 |3.22  |3.61  |4.89    |▁▃▆▇▇▃▁▁ |\n\n### Note\nThis dataset was automatically described using the [codebook R package](https://rubenarslan.github.io/codebook/) (version 0.8.1).",
  "identifier": "https://dx.doi.org/10.17605/OSF.IO/X25D3",
  "creator": "Julia C. Eberlen, Emmanuel Nicaise, Sarah Leveaux, Youri L. Mora, Olivier Klein",
  "citation": "Eberlen, J. C., Nicaise, E., Leveaux, S., Mora, Y., & Klein, O. (2019, August 5). Impact of Data sharing: Data collected offline. https://doi.org/10.17605/OSF.IO/X25D3",
  "datePublished": "2019-08-05",
  "temporalCoverage": "2018-12-03 to 2018-12-17",
  "spatialCoverage": "Campus Solbosch, Universite libre de Bruxelles, Brussels, Belgium",
  "keywords": ["V1", "id", "participant", "gender", "acad_status", "age", "refused", "reason", "consent", "bf_1", "bf_2R", "bf_3", "bf_4", "bf_5", "bf_6R", "bf_7", "bf_8R", "bf_9R", "bf_10", "bf_11", "bf_12R", "bf_13", "bf_14", "bf_15", "bf_16", "bf_17", "bf_18R", "bf_19", "bf_20", "bf_21R", "bf_22", "bf_23R", "bf_24R", "bf_25", "bf_26", "bf_27R", "bf_28", "bf_29", "bf_30", "bf_31R", "bf_32", "bf_33", "bf_34R", "bf_35R", "bf_36", "bf_37R", "bf_38R", "bf_39", "bf_40", "bf_41R", "bf_42", "bf_43R", "bf_44", "bf_45R", "cr_1", "cr_2", "cr_3", "cr_4", "cr_5", "cr_6", "cr_7", "soc_d_1", "soc_d_2R", "soc_d_3", "soc_d_4R", "soc_d_5R", "soc_d_6", "soc_d_7R", "soc_d_8", "soc_d_9", "soc_d_10R", "soc_d_11R", "soc_d_12", "soc_d_13R", "soc_d_14R", "soc_d_15", "soc_d_16", "soc_d_17R", "soc_d_18", "cond_anc", "anc_everest", "anc_chicago", "anc_bebe", "mc_1", "mc_2", "mc_3", "remarks", "Extraversion", "Agreeableness", "Conscientiousness", "Neuroticism", "Openness to experience", "Heteroduperie - Social Desirability"],
  "@context": "http://schema.org/",
  "@type": "Dataset",
  "variableMeasured": [
    {
      "name": "V1",
      "@type": "propertyValue"
    },
    {
      "name": "id",
      "@type": "propertyValue"
    },
    {
      "name": "participant",
      "@type": "propertyValue"
    },
    {
      "name": "gender",
      "description": "Reported gender information",
      "value": "1. Female,\n2. Male",
      "maxValue": 2,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "acad_status",
      "description": "Academic status",
      "value": "0. other,\n1. bachelor,\n2. master,\n3. PhD,\n4. other",
      "maxValue": 4,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "age",
      "description": "Age Group",
      "value": "1. 18-29 yo,\n2. 21-25 yo,\n3. 26-30 yo,\n4. 31-35 yo,\n5. 36-40 yo,\n6. 41-50 yo,\n7. 51-60 yo,\n8. 61-70 yo,\n9. 71-80 yo,\n10. 81-110 yo",
      "maxValue": 10,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "refused",
      "description": "Refused participation after consent",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "reason",
      "description": "Reason for refusal",
      "value": "0. Not refused,\n1. Data sharing,\n2. Not enough time,\n3. Other",
      "maxValue": 3,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "consent",
      "description": "Data sharing policy in consent",
      "value": "A. Data will be shared,\nB. Data will not be shared",
      "maxValue": "B",
      "minValue": "A",
      "@type": "propertyValue"
    },
    {
      "name": "bf_1",
      "description": "Big 5 Extraversion item 1",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_2R",
      "description": "Big 5 Agreeableness item 2",
      "value": "5. Disapprove strongly,\n4. Disapprove slightly,\n3. Neither approve nore disapprove,\n2. Approve slightly,\n1. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_3",
      "description": "Big 5 Conscientiousness item 3",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_4",
      "description": "Big 5 Neuroticism item 4",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_5",
      "description": "Big 5 Openness to experience item 5",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_6R",
      "description": "Big 5 Extraversion item 6",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_7",
      "description": "Big 5 Agreeableness item 7",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_8R",
      "description": "Big 5 Conscientiousness item 8",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_9R",
      "description": "Big 5 Neuroticism item 9",
      "value": "5. Disapprove strongly,\n4. Disapprove slightly,\n3. Neither approve nore disapprove,\n2. Approve slightly,\n1. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_10",
      "description": "Big 5 Openness to experience item 10",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_11",
      "description": "Big 5 Extraversion item 11",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_12R",
      "description": "Big 5 Agreeableness item 12",
      "value": "5. Disapprove strongly,\n4. Disapprove slightly,\n3. Neither approve nore disapprove,\n2. Approve slightly,\n1. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_13",
      "description": "Big 5 Conscientiousness item 13",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_14",
      "description": "Big 5 Neuroticism item 14",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_15",
      "description": "Big 5 Openness to experience item 15",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_16",
      "description": "Big 5 Extraversion item 16",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_17",
      "description": "Big 5 Agreeableness item 17",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_18R",
      "description": "Big 5 Conscientiousness item 18",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_19",
      "description": "Big 5 Neuroticism item 19",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_20",
      "description": "Big 5 Openness to experience item 20",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_21R",
      "description": "Big 5 Extraversion item 21",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_22",
      "description": "Big 5 Agreeableness item 22",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_23R",
      "description": "Big 5 Conscientiousness item 23",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_24R",
      "description": "Big 5 Neuroticism item 24",
      "value": "5. Disapprove strongly,\n4. Disapprove slightly,\n3. Neither approve nore disapprove,\n2. Approve slightly,\n1. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_25",
      "description": "Big 5 Openness to experience item 25",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_26",
      "description": "Big 5 Extraversion item 26",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_27R",
      "description": "Big 5 Agreeableness item 27",
      "value": "5. Disapprove strongly,\n4. Disapprove slightly,\n3. Neither approve nore disapprove,\n2. Approve slightly,\n1. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_28",
      "description": "Big 5 Conscientiousness item 28",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_29",
      "description": "Big 5 Neuroticism item 29",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_30",
      "description": "Big 5 Openness to experience item 30",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_31R",
      "description": "Big 5 Extraversion item 31",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_32",
      "description": "Big 5 Agreeableness item 32",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_33",
      "description": "Big 5 Conscientiousness item 33",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_34R",
      "description": "Big 5 Neuroticism item 34",
      "value": "5. Disapprove strongly,\n4. Disapprove slightly,\n3. Neither approve nore disapprove,\n2. Approve slightly,\n1. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_35R",
      "description": "Big 5 Openness to experience item 35",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_36",
      "description": "Big 5 Extraversion item 36",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_37R",
      "description": "Big 5 Agreeableness item 37",
      "value": "5. Disapprove strongly,\n4. Disapprove slightly,\n3. Neither approve nore disapprove,\n2. Approve slightly,\n1. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_38R",
      "description": "Big 5 Conscientiousness item 38",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_39",
      "description": "Big 5 Neuroticism item 39",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_40",
      "description": "Big 5 Openness to experience item 40",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_41R",
      "description": "Big 5 Openness to experience item 41",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_42",
      "description": "Big 5 Agreeableness item 42",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_43R",
      "description": "Big 5 Conscientiousness item 43",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_44",
      "description": "Big 5 Openness to experience item 44",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_45R",
      "description": "Big 5 Agreeableness item 45",
      "value": "5. Disapprove strongly,\n4. Disapprove slightly,\n3. Neither approve nore disapprove,\n2. Approve slightly,\n1. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "cr_1",
      "description": "Careless response item 1",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "cr_2",
      "description": "Careless response item 2",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "cr_3",
      "description": "Careless response item 3",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "cr_4",
      "description": "Careless response item 4",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "cr_5",
      "description": "Careless response item 5",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "cr_6",
      "description": "Careless response item 6",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "cr_7",
      "description": "Careless response item 7",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_1",
      "description": "Social desirability item 1",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_2R",
      "description": "Social desirability item 2",
      "value": "5. Disapprove strongly,\n4. Disapprove slightly,\n3. Neither approve nore disapprove,\n2. Approve slightly,\n1. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_3",
      "description": "Social desirability item 3",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_4R",
      "description": "Social desirability item 4",
      "value": "5. Disapprove strongly,\n4. Disapprove slightly,\n3. Neither approve nore disapprove,\n2. Approve slightly,\n1. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_5R",
      "description": "Social desirability item 5",
      "value": "5. Disapprove strongly,\n4. Disapprove slightly,\n3. Neither approve nore disapprove,\n2. Approve slightly,\n1. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_6",
      "description": "Social desirability item 6",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_7R",
      "description": "Social desirability item 7",
      "value": "5. Disapprove strongly,\n4. Disapprove slightly,\n3. Neither approve nore disapprove,\n2. Approve slightly,\n1. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_8",
      "description": "Social desirability item 8",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_9",
      "description": "Social desirability item 9",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_10R",
      "description": "Social desirability item 10",
      "value": "5. Disapprove strongly,\n4. Disapprove slightly,\n3. Neither approve nore disapprove,\n2. Approve slightly,\n1. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_11R",
      "description": "Social desirability item 11",
      "value": "5. Disapprove strongly,\n4. Disapprove slightly,\n3. Neither approve nore disapprove,\n2. Approve slightly,\n1. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_12",
      "description": "Social desirability item 12",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_13R",
      "description": "Social desirability item 13",
      "value": "5. Disapprove strongly,\n4. Disapprove slightly,\n3. Neither approve nore disapprove,\n2. Approve slightly,\n1. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_14R",
      "description": "Social desirability item 14",
      "value": "5. Disapprove strongly,\n4. Disapprove slightly,\n3. Neither approve nore disapprove,\n2. Approve slightly,\n1. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_15",
      "description": "Social desirability item 15",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_16",
      "description": "Social desirability item 16",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_17R",
      "description": "Social desirability item 17",
      "value": "5. Disapprove strongly,\n4. Disapprove slightly,\n3. Neither approve nore disapprove,\n2. Approve slightly,\n1. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_18",
      "description": "Social desirability item 18",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "cond_anc",
      "description": "Anchoring condition",
      "value": "0. Low anchoring value,\n1. High anchoring value",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "anc_everest",
      "description": "Anchoring: How high is Mount Everest",
      "@type": "propertyValue"
    },
    {
      "name": "anc_chicago",
      "description": "Anchoring: Population of Chicagor",
      "@type": "propertyValue"
    },
    {
      "name": "anc_bebe",
      "description": "Anchoring: Babies born in US",
      "@type": "propertyValue"
    },
    {
      "name": "mc_1",
      "description": "Manipulation check, question 1: 'Do you remember the consent you signed in the beginning?'",
      "value": "1. Yes,\n0. No",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "mc_2",
      "description": "Manipulation check, question 2: 'Do you remember if the consent contained the topic of sharing anonymous data with others?' ",
      "value": "1. Yes,\n0. No",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "mc_3",
      "description": "Manipulation check, question: 'Will your anonymous data be shared with others?'",
      "value": "1. Yes,\n0. No",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "remarks",
      "description": "Observer remarks",
      "@type": "propertyValue"
    },
    {
      "name": "Extraversion",
      "description": "8 bf items aggregated by rowMeans",
      "@type": "propertyValue"
    },
    {
      "name": "Agreeableness",
      "description": "10 bf items aggregated by rowMeans",
      "@type": "propertyValue"
    },
    {
      "name": "Conscientiousness",
      "description": "9 bf items aggregated by rowMeans",
      "@type": "propertyValue"
    },
    {
      "name": "Neuroticism",
      "description": "8 bf items aggregated by rowMeans",
      "@type": "propertyValue"
    },
    {
      "name": "Openness to experience",
      "description": "10 bf items aggregated by rowMeans",
      "@type": "propertyValue"
    },
    {
      "name": "Heteroduperie - Social Desirability",
      "description": "18 soc_d items aggregated by rowMeans",
      "@type": "propertyValue"
    }
  ]
}`